EU-AIMS ADI-R Subtyping Connectivity Analysis
easypackages::libraries(c("here","ggplot2","nlme","readxl","matlabr","circlize","scico"))
source("/Users/mlombardo/Dropbox/GitHubRepos/utils/cohens_d.R")
source("/Users/mlombardo/Dropbox/R/Repfunctionspack6.R")
source("/Users/mlombardo/Dropbox/R/get_ggColorHue.R")
fdr_thresh = 0.05
options(matlab.path = "/Applications/MATLAB_R2019b.app/bin")
rootpath = "/Users/mlombardo/Dropbox/euaims/data/adir"
datapath = here("data")
codepath = here("code")
resultpath = here("results")
plotpath = here("plots")
Run the MATLAB script that estimates the partial correlations
RUNMATLAB = FALSE
if (RUNMATLAB){
# z = 0.5
code2run = sprintf("cd %s; estimateConnectivity_z05('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 0.6
code2run = sprintf("cd %s; estimateConnectivity_z06('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 0.7
code2run = sprintf("cd %s; estimateConnectivity_z07('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 0.8
code2run = sprintf("cd %s; estimateConnectivity_z08('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 0.9
code2run = sprintf("cd %s; estimateConnectivity_z09('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 1
code2run = sprintf("cd %s; estimateConnectivity_z1('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
}
Main analysis - Z = 0.5
# Z threshold
z_thresh = 0.5
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC06 IC01_IC06 1.8413429 0.067533513
## IC01_IC17 IC01_IC17 1.5666812 0.119282487
## IC03_IC12 IC03_IC12 2.8401768 0.005131071
## IC03_IC13 IC03_IC13 2.2147343 0.028276159
## IC04_IC12 IC04_IC12 1.3828595 0.168749492
## IC07_IC13 IC07_IC13 -2.8083624 0.005637955
## IC08_IC13 IC08_IC13 -0.3378935 0.735912773
## IC11_IC17 IC11_IC17 1.8988090 0.059497073
## IC12_IC17 IC12_IC17 1.2111391 0.227734188
## IC13_IC14 IC13_IC14 -1.8206843 0.070634676
## IC14_IC16 IC14_IC16 -3.1379365 0.002046295
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC06 -0.31589407 44.16421
## IC01_IC17 -0.31085336 42.95287
## IC03_IC12 -0.52491355 36.32295
## IC03_IC13 -0.40328866 -66.73545
## IC04_IC12 -0.27533235 20.61361
## IC07_IC13 0.46427323 -35.40795
## IC08_IC13 0.05698519 -82.82008
## IC11_IC17 -0.36550529 114.54788
## IC12_IC17 -0.22269681 53.46934
## IC13_IC14 0.32622066 -183.60601
## IC14_IC16 0.59459741 -186.70432
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC06 62.42476 2.6363624
## IC01_IC17 61.21342 2.3276059
## IC03_IC12 54.58350 2.5555425
## IC03_IC13 -48.47490 1.3725889
## IC04_IC12 38.87416 2.6556596
## IC07_IC13 -17.14740 -2.9826343
## IC08_IC13 -64.55952 -0.2485671
## IC11_IC17 132.80843 -0.1204165
## IC12_IC17 71.72989 1.1570772
## IC13_IC14 -165.34546 -2.2360710
## IC14_IC16 -168.44377 -2.5131329
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC06 0.009216116 -0.44305525
## IC01_IC17 0.021201529 -0.40944267
## IC03_IC12 0.011546013 -0.44816623
## IC03_IC13 0.171825394 -0.26660805
## IC04_IC12 0.008726603 -0.48080671
## IC07_IC13 0.003311324 0.53282941
## IC08_IC13 0.804018491 0.03831356
## IC11_IC17 0.904306204 0.01374379
## IC12_IC17 0.248987131 -0.21085918
## IC13_IC14 0.026747795 0.37904537
## IC14_IC16 0.012968756 0.43149673
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC06 60.451246 78.97682 18.7576866
## IC01_IC17 8.734708 27.26029 8.9771527
## IC03_IC12 29.532908 48.05849 16.2822748
## IC03_IC13 -129.435841 -110.91026 1.4112741
## IC04_IC12 32.691545 51.21712 15.7607913
## IC07_IC13 -95.441355 -76.91578 52.9392129
## IC08_IC13 -114.380991 -95.85541 0.7067550
## IC11_IC17 137.674609 156.20019 0.2381482
## IC12_IC17 26.012760 44.53834 1.3365816
## IC13_IC14 -165.132193 -146.60661 7.9407844
## IC14_IC16 -169.847536 -151.32196 13.1868082
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC06 0.7572340 0.449842834
## IC01_IC17 0.4593805 0.646483354
## IC03_IC12 2.0187698 0.044909445
## IC03_IC13 2.6093897 0.009788873
## IC04_IC12 1.7982573 0.073715378
## IC07_IC13 -1.6891351 0.092825290
## IC08_IC13 1.1404445 0.255529170
## IC11_IC17 1.8352267 0.068027294
## IC12_IC17 2.3199351 0.021401223
## IC13_IC14 -2.0447737 0.042249033
## IC14_IC16 -2.4362432 0.015757921
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC06 -0.08383745 15.16530
## IC01_IC17 -0.05621161 90.02013
## IC03_IC12 -0.28401316 50.42322
## IC03_IC13 -0.36665348 -105.62195
## IC04_IC12 -0.27682551 26.79304
## IC07_IC13 0.22091396 -58.55765
## IC08_IC13 -0.17852219 -110.99064
## IC11_IC17 -0.25174213 148.75789
## IC12_IC17 -0.34180496 56.50485
## IC13_IC14 0.29431425 -234.25081
## IC14_IC16 0.33880867 -236.85212
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC06 34.80330 -0.1760962
## IC01_IC17 109.65813 2.9866349
## IC03_IC12 70.06122 1.4449144
## IC03_IC13 -85.98396 2.7443254
## IC04_IC12 46.43104 1.3442740
## IC07_IC13 -38.91965 -3.5391276
## IC08_IC13 -91.35264 2.7217617
## IC11_IC17 168.39589 2.3422629
## IC12_IC17 76.14285 3.4007708
## IC13_IC14 -214.61282 -2.5826298
## IC14_IC16 -217.21412 -1.9903581
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC06 0.8604001473 0.03151866
## IC01_IC17 0.0031806569 -0.39211560
## IC03_IC12 0.1500787616 -0.20805161
## IC03_IC13 0.0066267129 -0.38565128
## IC04_IC12 0.1804136252 -0.18329723
## IC07_IC13 0.0005013321 0.49701868
## IC08_IC13 0.0070784513 -0.38673454
## IC11_IC17 0.0201706598 -0.31974650
## IC12_IC17 0.0008142816 -0.44419738
## IC13_IC14 0.0105337161 0.36790730
## IC14_IC16 0.0479411986 0.29499465
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC06 64.05820 83.84810 0.5705348
## IC01_IC17 22.87360 42.66350 11.9310339
## IC03_IC12 15.20130 34.99120 1.8168802
## IC03_IC13 -164.62530 -144.83539 28.4982733
## IC04_IC12 41.27765 61.06755 1.6309456
## IC07_IC13 -92.10527 -72.31536 139.4856221
## IC08_IC13 -106.02197 -86.23206 14.8551875
## IC11_IC17 171.15950 190.94940 10.0034088
## IC12_IC17 18.99499 38.78489 151.7218503
## IC13_IC14 -235.90005 -216.11015 17.6910070
## IC14_IC16 -221.89274 -202.10284 4.7217566
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC06 0.96044794 0.3389555
## IC01_IC17 0.96148792 0.3384350
## IC03_IC12 1.11565258 0.2670245
## IC03_IC13 0.07808261 0.9379055
## IC04_IC12 -0.20532914 0.8376979
## IC07_IC13 -0.99012090 0.3243079
## IC08_IC13 -1.11081930 0.2690905
## IC11_IC17 0.40321789 0.6875778
## IC12_IC17 -0.61611145 0.5391055
## IC13_IC14 -0.22061138 0.8258077
## IC14_IC16 -1.31139157 0.1924812
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC06 -0.228902618 52.107351
## IC01_IC17 -0.204018229 84.797584
## IC03_IC12 -0.217240446 49.153699
## IC03_IC13 -0.044318968 -29.145006
## IC04_IC12 0.009801726 65.683694
## IC07_IC13 0.235977357 -9.320828
## IC08_IC13 0.225051320 -36.378068
## IC11_IC17 -0.109625877 78.425507
## IC12_IC17 0.122934322 42.704912
## IC13_IC14 0.029911084 -156.737603
## IC14_IC16 0.234862038 -117.257104
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC06 68.418344 2.67891056
## IC01_IC17 101.108577 -0.18684414
## IC03_IC12 65.464693 1.24865139
## IC03_IC13 -12.834013 -0.68326938
## IC04_IC12 81.994688 1.44574773
## IC07_IC13 6.990165 0.01194164
## IC08_IC13 -20.067075 -2.27939870
## IC11_IC17 94.736501 -2.06457055
## IC12_IC17 59.015905 -1.25724278
## IC13_IC14 -140.426610 0.16737482
## IC14_IC16 -100.946110 -0.57184903
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC06 0.00843168 -0.523643178
## IC01_IC17 0.85210101 0.008086164
## IC03_IC12 0.21424351 -0.215835437
## IC03_IC13 0.49576481 0.121798397
## IC04_IC12 0.15087648 -0.277527405
## IC07_IC13 0.99049218 -0.009221918
## IC08_IC13 0.02442518 0.420033071
## IC11_IC17 0.04113654 0.357550945
## IC12_IC17 0.21112764 0.216749249
## IC13_IC14 0.86735923 0.031441376
## IC14_IC16 0.56850289 0.148429035
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC06 30.54137 47.365500
## IC01_IC17 25.18611 42.010238
## IC03_IC12 63.06124 79.885368
## IC03_IC13 -78.79223 -61.968101
## IC04_IC12 46.26644 63.090570
## IC07_IC13 -19.04980 -2.225672
## IC08_IC13 -48.77397 -31.949846
## IC11_IC17 103.26047 120.084592
## IC12_IC17 43.91449 60.738618
## IC13_IC14 -139.35921 -122.535080
## IC14_IC16 -139.67194 -122.847810
## SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC06 12.0374937
## IC01_IC17 0.4961466
## IC03_IC12 1.4947995
## IC03_IC13 0.7565176
## IC04_IC12 1.0152675
## IC07_IC13 0.5258595
## IC08_IC13 6.6650092
## IC11_IC17 1.3195485
## IC12_IC17 1.3860308
## IC13_IC14 0.6703009
## IC14_IC16 0.6902873
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC06 IC01_IC06 18.7576866 0.5705348
## IC01_IC17 IC01_IC17 8.9771527 11.9310339
## IC03_IC12 IC03_IC12 16.2822748 1.8168802
## IC03_IC13 IC03_IC13 1.4112741 28.4982733
## IC04_IC12 IC04_IC12 15.7607913 1.6309456
## IC07_IC13 IC07_IC13 52.9392129 139.4856221
## IC08_IC13 IC08_IC13 0.7067550 14.8551875
## IC11_IC17 IC11_IC17 0.2381482 10.0034088
## IC12_IC17 IC12_IC17 1.3365816 151.7218503
## IC13_IC14 IC13_IC14 7.9407844 17.6910070
## IC14_IC16 IC14_IC16 13.1868082 4.7217566
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 0.6
# Z threshold
z_thresh = 0.6
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC06 IC01_IC06 1.66254822 0.098348538
## IC01_IC17 IC01_IC17 0.79415989 0.428271355
## IC03_IC12 IC03_IC12 2.48017473 0.014160627
## IC03_IC13 IC03_IC13 2.09875540 0.037398931
## IC05_IC11 IC05_IC11 -2.66151777 0.008567424
## IC07_IC13 IC07_IC13 -2.89348322 0.004337998
## IC08_IC13 IC08_IC13 0.07297889 0.941913500
## IC12_IC17 IC12_IC17 0.84206122 0.401002769
## IC13_IC14 IC13_IC14 -1.46699958 0.144326819
## IC14_IC16 IC14_IC16 -2.72038159 0.007237421
## IC17_IC18 IC17_IC18 2.77030543 0.006259178
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC06 -0.25852192 45.21375
## IC01_IC17 -0.14471172 55.67809
## IC03_IC12 -0.41947863 37.64433
## IC03_IC13 -0.35116166 -80.29124
## IC05_IC11 0.43039717 148.39503
## IC07_IC13 0.44878483 -45.20098
## IC08_IC13 -0.01507376 -91.87089
## IC12_IC17 -0.14260614 65.76743
## IC13_IC14 0.23486852 -198.80248
## IC14_IC16 0.47053788 -195.47539
## IC17_IC18 -0.46470551 62.89701
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC06 63.84943 2.4961847
## IC01_IC17 74.31376 1.8472649
## IC03_IC12 56.28000 2.5343524
## IC03_IC13 -61.65557 1.8102804
## IC05_IC11 167.03070 0.5689963
## IC07_IC13 -26.56531 -2.7969355
## IC08_IC13 -73.23522 -0.2070050
## IC12_IC17 84.40310 1.2522290
## IC13_IC14 -180.16681 -2.8143652
## IC14_IC16 -176.83971 -2.6525964
## IC17_IC18 81.53268 2.1449450
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC06 0.013529643 -0.39322901
## IC01_IC17 0.066488695 -0.29199258
## IC03_IC12 0.012190809 -0.41328120
## IC03_IC13 0.072060812 -0.32638966
## IC05_IC11 0.570127679 -0.12044979
## IC07_IC13 0.005768569 0.46791755
## IC08_IC13 0.836259690 0.01988449
## IC12_IC17 0.212247854 -0.22309547
## IC13_IC14 0.005478483 0.45447878
## IC14_IC16 0.008762645 0.41803385
## IC17_IC18 0.033411491 -0.33705288
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC06 57.52452 76.33931 12.95832239
## IC01_IC17 19.72037 38.53516 2.93775710
## IC03_IC12 33.17553 51.99032 16.46792960
## IC03_IC13 -126.63378 -107.81898 3.44278123
## IC05_IC11 113.94220 132.75699 0.05892928
## IC07_IC13 -101.29119 -82.47640 31.97489637
## IC08_IC13 -109.76437 -90.94958 0.69791010
## IC12_IC17 22.57417 41.38896 1.47226933
## IC13_IC14 -178.33909 -159.52430 22.59791876
## IC14_IC16 -185.63020 -166.81541 21.98868108
## IC17_IC18 32.87683 51.69162 6.00923248
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC06 0.8808227 0.379556678
## IC01_IC17 0.6160951 0.538589035
## IC03_IC12 2.1268975 0.034753760
## IC03_IC13 2.7839134 0.005928823
## IC05_IC11 -1.9327690 0.054791642
## IC07_IC13 -1.5816917 0.115428095
## IC08_IC13 1.0485194 0.295767220
## IC12_IC17 2.1611994 0.031965059
## IC13_IC14 -2.2745029 0.024083487
## IC14_IC16 -2.4084524 0.017001826
## IC17_IC18 3.1078386 0.002182117
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC06 -0.11238664 12.83930
## IC01_IC17 -0.09209342 84.31261
## IC03_IC12 -0.31057674 51.05153
## IC03_IC13 -0.39836167 -99.21376
## IC05_IC11 0.25989884 176.33365
## IC07_IC13 0.20549220 -52.56735
## IC08_IC13 -0.16192331 -106.51183
## IC12_IC17 -0.32432466 58.92219
## IC13_IC14 0.33697796 -224.96543
## IC14_IC16 0.34429162 -230.99690
## IC17_IC18 -0.44888984 49.70195
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC06 32.28978 -0.2422423
## IC01_IC17 103.76309 3.0525970
## IC03_IC12 70.50201 1.3701996
## IC03_IC13 -79.76328 2.7426661
## IC05_IC11 195.78413 -2.3497592
## IC07_IC13 -33.11686 -3.6032261
## IC08_IC13 -87.06135 2.8430103
## IC12_IC17 78.37267 3.3915343
## IC13_IC14 -205.51495 -2.1997532
## IC14_IC16 -211.54642 -1.9268669
## IC17_IC18 69.15243 2.4021744
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC06 0.8088550619 0.04498497
## IC01_IC17 0.0025955555 -0.42213806
## IC03_IC12 0.1722499778 -0.20161752
## IC03_IC13 0.0066803760 -0.39049183
## IC05_IC11 0.0198173583 0.34354024
## IC07_IC13 0.0004018150 0.52570319
## IC08_IC13 0.0049604164 -0.41342284
## IC12_IC17 0.0008463575 -0.45816563
## IC13_IC14 0.0290371423 0.31371071
## IC14_IC16 0.0554959908 0.28752789
## IC17_IC18 0.0172660650 -0.34222028
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC06 66.916997 86.49314 0.5253045
## IC01_IC17 25.560412 45.13655 15.9854247
## IC03_IC12 9.693625 29.26977 1.5382473
## IC03_IC13 -156.466683 -136.89054 28.3825036
## IC05_IC11 152.597179 172.17332 10.3605754
## IC07_IC13 -86.723771 -67.14763 146.3930844
## IC08_IC13 -109.761729 -90.18559 17.2332532
## IC12_IC17 20.818236 40.39438 134.8148356
## IC13_IC14 -225.270741 -205.69460 7.6815398
## IC14_IC16 -208.133690 -188.55755 4.1524626
## IC17_IC18 42.412634 61.98877 10.5815455
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC06 0.6727654 0.5024700
## IC01_IC17 0.2166683 0.8288575
## IC03_IC12 0.5127720 0.6091118
## IC03_IC13 -0.3369085 0.7368108
## IC05_IC11 -0.8539263 0.3949525
## IC07_IC13 -1.1602716 0.2483837
## IC08_IC13 -0.7590929 0.4493775
## IC12_IC17 -0.9263115 0.3562583
## IC13_IC14 0.6753604 0.5008265
## IC14_IC16 -0.7559547 0.4512494
## IC17_IC18 0.1960251 0.8449426
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC06 -0.15079098 51.26289
## IC01_IC17 -0.03899752 88.17773
## IC03_IC12 -0.09246625 50.34586
## IC03_IC13 0.04528777 -35.35772
## IC05_IC11 0.15905282 121.65789
## IC07_IC13 0.23011161 -13.47202
## IC08_IC13 0.14181483 -40.66506
## IC12_IC17 0.17389515 57.45723
## IC13_IC14 -0.12715793 -162.71368
## IC14_IC16 0.12024751 -119.23613
## IC17_IC18 -0.04217918 12.33623
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC06 67.784432 2.45688686
## IC01_IC17 104.699270 -0.79020048
## IC03_IC12 66.867405 1.18290539
## IC03_IC13 -18.836182 -0.33453564
## IC05_IC11 138.179433 2.81601484
## IC07_IC13 3.049524 0.45466195
## IC08_IC13 -24.143519 -2.33959265
## IC12_IC17 73.978776 -1.27310054
## IC13_IC14 -146.192138 -0.77863750
## IC14_IC16 -102.714592 -0.64941699
## IC17_IC18 28.857776 -0.02695177
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC06 0.015444928 -0.47912367
## IC01_IC17 0.430969482 0.13465623
## IC03_IC12 0.239184975 -0.19926175
## IC03_IC13 0.738559397 0.05855422
## IC05_IC11 0.005686161 -0.45450933
## IC07_IC13 0.650173873 -0.08947701
## IC08_IC13 0.020957137 0.42164800
## IC12_IC17 0.205443187 0.22235722
## IC13_IC14 0.437725972 0.16660735
## IC14_IC16 0.517309992 0.13193024
## IC17_IC18 0.978542972 0.01548788
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC06 30.99300 47.866102
## IC01_IC17 37.21348 54.086587
## IC03_IC12 62.77505 79.648157
## IC03_IC13 -68.36771 -51.494604
## IC05_IC11 110.35613 127.229240
## IC07_IC13 -19.72100 -2.847891
## IC08_IC13 -49.52959 -32.656479
## IC12_IC17 43.03561 59.908720
## IC13_IC14 -142.32967 -125.456560
## IC14_IC16 -141.93115 -125.058042
## IC17_IC18 45.16982 62.042924
## SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC06 6.4250267
## IC01_IC17 0.7412616
## IC03_IC12 1.2623906
## IC03_IC13 0.7355969
## IC05_IC11 1.2656620
## IC07_IC13 0.3979671
## IC08_IC13 5.7544099
## IC12_IC17 1.5244974
## IC13_IC14 0.5567477
## IC14_IC16 0.8553861
## IC17_IC18 0.6872281
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC06 IC01_IC06 12.95832239 0.5253045
## IC01_IC17 IC01_IC17 2.93775710 15.9854247
## IC03_IC12 IC03_IC12 16.46792960 1.5382473
## IC03_IC13 IC03_IC13 3.44278123 28.3825036
## IC05_IC11 IC05_IC11 0.05892928 10.3605754
## IC07_IC13 IC07_IC13 31.97489637 146.3930844
## IC08_IC13 IC08_IC13 0.69791010 17.2332532
## IC12_IC17 IC12_IC17 1.47226933 134.8148356
## IC13_IC14 IC13_IC14 22.59791876 7.6815398
## IC14_IC16 IC14_IC16 21.98868108 4.1524626
## IC17_IC18 IC17_IC18 6.00923248 10.5815455
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 0.7
# Z threshold
z_thresh = 0.7
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC03_IC12 IC03_IC12 2.3221454 0.021417719
## IC03_IC13 IC03_IC13 2.0756019 0.039445282
## IC05_IC06 IC05_IC06 -1.0880070 0.278141201
## IC07_IC13 IC07_IC13 -2.8899015 0.004359289
## IC08_IC13 IC08_IC13 0.5614606 0.575227364
## IC12_IC17 IC12_IC17 0.9643378 0.336254474
## IC13_IC14 IC13_IC14 -1.9731060 0.050114214
## IC17_IC18 IC17_IC18 3.0818332 0.002402717
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC03_IC12 -0.37938636 33.60332
## IC03_IC13 -0.33207572 -92.01983
## IC05_IC06 0.17355520 144.86564
## IC07_IC13 0.43340407 -45.05961
## IC08_IC13 -0.08830724 -97.68590
## IC12_IC17 -0.15543100 61.97986
## IC13_IC14 0.30097111 -213.99985
## IC17_IC18 -0.48912800 60.90796
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC03_IC12 52.52307 2.5755098
## IC03_IC13 -73.10008 1.8029660
## IC05_IC06 163.78539 -2.5340428
## IC07_IC13 -26.13986 -3.2756600
## IC08_IC13 -78.76615 0.4297624
## IC12_IC17 80.89961 0.9077748
## IC13_IC14 -195.08010 -2.9243534
## IC17_IC18 79.82770 1.9834594
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC03_IC12 0.010830672 -0.40226600
## IC03_IC13 0.073104214 -0.31025425
## IC05_IC06 0.012147823 0.38582603
## IC07_IC13 0.001269619 0.50957403
## IC08_IC13 0.667894165 -0.08253222
## IC12_IC17 0.365238501 -0.16341147
## IC13_IC14 0.003906066 0.44677373
## IC17_IC18 0.048871280 -0.29935754
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC03_IC12 28.13199 47.28974 18.109006
## IC03_IC13 -142.87159 -123.71385 3.394453
## IC05_IC06 121.03319 140.19093 10.257489
## IC07_IC13 -106.48364 -87.32590 126.898503
## IC08_IC13 -99.99569 -80.83795 0.757630
## IC12_IC17 26.27354 45.43128 1.047415
## IC13_IC14 -198.61959 -179.46185 38.176593
## IC17_IC18 39.19986 58.35761 3.445757
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC03_IC12 2.08229921 0.038729153
## IC03_IC13 2.77204527 0.006156424
## IC05_IC06 0.09497558 0.924439872
## IC07_IC13 -1.52102284 0.130008812
## IC08_IC13 0.74973991 0.454390144
## IC12_IC17 2.02740553 0.044095734
## IC13_IC14 -2.23495975 0.026649776
## IC17_IC18 2.90490403 0.004134334
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC03_IC12 -0.31137544 54.36135
## IC03_IC13 -0.40294746 -92.31312
## IC05_IC06 -0.01273963 126.35970
## IC07_IC13 0.20454946 -56.21310
## IC08_IC13 -0.12995742 -101.94828
## IC12_IC17 -0.31061496 62.05198
## IC13_IC14 0.33615135 -213.21721
## IC17_IC18 -0.43594941 51.03480
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC03_IC12 73.65096 1.444407
## IC03_IC13 -73.02350 2.780506
## IC05_IC06 145.64932 -3.140018
## IC07_IC13 -36.92349 -3.053138
## IC08_IC13 -82.65867 2.733688
## IC12_IC17 81.34159 3.562849
## IC13_IC14 -193.92759 -1.854706
## IC17_IC18 70.32441 2.486167
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC03_IC12 0.1503440011 -0.2113858
## IC03_IC13 0.0059982978 -0.4022988
## IC05_IC06 0.0019717131 0.4609681
## IC07_IC13 0.0026041380 0.4708069
## IC08_IC13 0.0068813790 -0.4078235
## IC12_IC17 0.0004684346 -0.5011120
## IC13_IC14 0.0652558885 0.2808212
## IC17_IC18 0.0138140386 -0.3457352
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC03_IC12 15.58339 34.93787 1.796769
## IC03_IC13 -145.79806 -126.44358 31.502747
## IC05_IC06 120.12686 139.48134 6.699426
## IC07_IC13 -84.18131 -64.82683 38.318912
## IC08_IC13 -102.15276 -82.79828 10.640599
## IC12_IC17 16.40916 35.76364 190.471531
## IC13_IC14 -210.19769 -190.84320 3.745154
## IC17_IC18 36.47313 55.82761 14.191801
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC03_IC12 0.3190830 0.7502375
## IC03_IC13 -0.4926518 0.6231898
## IC05_IC06 -1.0641113 0.2894883
## IC07_IC13 -1.1850184 0.2384323
## IC08_IC13 -0.2468525 0.8054587
## IC12_IC17 -0.8468887 0.3988010
## IC13_IC14 0.2802622 0.7797756
## IC17_IC18 0.2959209 0.7678196
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC03_IC12 -0.04668964 49.95491
## IC03_IC13 0.07778101 -40.12727
## IC05_IC06 0.19161003 105.10159
## IC07_IC13 0.22658887 -16.89676
## IC08_IC13 0.04135501 -43.14922
## IC12_IC17 0.15370190 56.69659
## IC13_IC14 -0.05724727 -164.95111
## IC17_IC18 -0.07916051 10.80373
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC03_IC12 66.6296490 1.14189559
## IC03_IC13 -23.4525241 -0.50949046
## IC05_IC06 121.7763292 0.54424508
## IC07_IC13 -0.2220195 -0.08776844
## IC08_IC13 -26.4744765 -1.74811838
## IC12_IC17 73.3713349 -1.75752310
## IC13_IC14 -148.2763723 -0.85599717
## IC17_IC18 27.4784667 -0.07494163
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC03_IC12 0.25571552 -0.17433072
## IC03_IC13 0.61132136 0.09533090
## IC05_IC06 0.58725809 -0.07039771
## IC07_IC13 0.93020345 0.00370345
## IC08_IC13 0.08293872 0.29941140
## IC12_IC17 0.08131659 0.31020269
## IC13_IC14 0.39366383 0.17936131
## IC17_IC18 0.94038290 0.04383111
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC03_IC12 62.47247 79.490164
## IC03_IC13 -73.47254 -56.454847
## IC05_IC06 108.47343 125.491121
## IC07_IC13 -21.93991 -4.922215
## IC08_IC13 -35.11306 -18.095367
## IC12_IC17 43.21120 60.228892
## IC13_IC14 -147.24087 -130.223174
## IC17_IC18 45.73968 62.757368
## SCequalRRB_vs_SCoverRRB.repBF
## IC03_IC12 1.1392680
## IC03_IC13 0.7924462
## IC05_IC06 0.4206097
## IC07_IC13 0.5118080
## IC08_IC13 1.8559685
## IC12_IC17 2.6666260
## IC13_IC14 0.7316458
## IC17_IC18 0.6739529
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC03_IC12 IC03_IC12 18.109006 1.796769
## IC03_IC13 IC03_IC13 3.394453 31.502747
## IC05_IC06 IC05_IC06 10.257489 6.699426
## IC07_IC13 IC07_IC13 126.898503 38.318912
## IC08_IC13 IC08_IC13 0.757630 10.640599
## IC12_IC17 IC12_IC17 1.047415 190.471531
## IC13_IC14 IC13_IC14 38.176593 3.745154
## IC17_IC18 IC17_IC18 3.445757 14.191801
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 0.8
# Z threshold
z_thresh = 0.8
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC17 IC01_IC17 1.2562834 0.210659538
## IC03_IC12 IC03_IC12 2.4428286 0.015549238
## IC03_IC13 IC03_IC13 1.7104317 0.088928359
## IC04_IC12 IC04_IC12 1.4303494 0.154369816
## IC05_IC06 IC05_IC06 -1.2455654 0.214561048
## IC07_IC13 IC07_IC13 -2.5605860 0.011279614
## IC08_IC13 IC08_IC13 1.1631086 0.246342333
## IC08_IC18 IC08_IC18 -0.4384108 0.661620105
## IC12_IC17 IC12_IC17 0.8173553 0.414818108
## IC13_IC14 IC13_IC14 -1.9140013 0.057224312
## IC14_IC20 IC14_IC20 0.6923233 0.489636515
## IC17_IC18 IC17_IC18 3.0803749 0.002395653
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC17 -0.20835801 54.78516
## IC03_IC12 -0.36186591 31.06959
## IC03_IC13 -0.25771857 -95.55475
## IC04_IC12 -0.22557859 14.93006
## IC05_IC06 0.18706974 151.49343
## IC07_IC13 0.37140570 -51.87107
## IC08_IC13 -0.17850241 -99.99358
## IC08_IC18 0.08530723 -69.03342
## IC12_IC17 -0.12489700 65.01258
## IC13_IC14 0.27458970 -224.28098
## IC14_IC20 -0.10607496 -183.73910
## IC17_IC18 -0.46164136 57.02774
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC17 74.00920 2.637062
## IC03_IC12 50.29363 2.373383
## IC03_IC13 -76.33071 1.432138
## IC04_IC12 34.15410 2.491532
## IC05_IC06 170.71747 -2.976618
## IC07_IC13 -32.64703 -3.293250
## IC08_IC13 -80.76954 0.166787
## IC08_IC18 -49.80938 -1.436088
## IC12_IC17 84.23662 1.497541
## IC13_IC14 -205.05694 -3.158892
## IC14_IC20 -164.51506 2.833198
## IC17_IC18 76.25178 2.484298
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC17 0.009089954 -0.38393257
## IC03_IC12 0.018673812 -0.35897661
## IC03_IC13 0.153829120 -0.24667367
## IC04_IC12 0.013619095 -0.39127290
## IC05_IC06 0.003313159 0.43949169
## IC07_IC13 0.001191118 0.49542376
## IC08_IC13 0.867723818 -0.04331143
## IC08_IC18 0.152703453 0.21022948
## IC12_IC17 0.135994384 -0.24516424
## IC13_IC14 0.001856199 0.47248954
## IC14_IC20 0.005131445 -0.41531301
## IC17_IC18 0.013889346 -0.36786494
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC17 12.67722 31.99935 13.6829890
## IC03_IC12 27.45724 46.77938 11.3486699
## IC03_IC13 -148.63220 -129.31006 1.9106092
## IC04_IC12 38.31937 57.64150 11.5035145
## IC05_IC06 122.45108 141.77322 26.2974372
## IC07_IC13 -113.06599 -93.74386 123.1232890
## IC08_IC13 -105.24428 -85.92214 0.5539346
## IC08_IC18 -84.92466 -65.60252 1.5428038
## IC12_IC17 38.56869 57.89083 1.9229706
## IC13_IC14 -207.76974 -188.44760 64.1224204
## IC14_IC20 -202.01336 -182.69122 11.9205026
## IC17_IC18 38.51395 57.83609 13.4117521
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC17 0.35605317 0.722234562
## IC03_IC12 2.02381035 0.044528297
## IC03_IC13 3.26518020 0.001318759
## IC04_IC12 2.10298661 0.036913618
## IC05_IC06 0.07101364 0.943468925
## IC07_IC13 -1.64686267 0.101401555
## IC08_IC13 0.40705983 0.684467243
## IC08_IC18 0.55646363 0.578613028
## IC12_IC17 2.38451605 0.018183591
## IC13_IC14 -2.47530591 0.014275049
## IC14_IC20 1.94782403 0.053054468
## IC17_IC18 2.91480522 0.004030075
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC17 -0.048088067 82.28289
## IC03_IC12 -0.316928373 55.19052
## IC03_IC13 -0.501232848 -88.69614
## IC04_IC12 -0.336009224 31.19146
## IC05_IC06 -0.008495749 123.84602
## IC07_IC13 0.224578577 -50.70787
## IC08_IC13 -0.067864543 -98.86840
## IC08_IC18 -0.101545823 -44.95714
## IC12_IC17 -0.375226524 57.40837
## IC13_IC14 0.383416959 -203.07557
## IC14_IC20 -0.312012136 -211.78360
## IC17_IC18 -0.453810343 54.90991
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC17 101.33979 2.6506078
## IC03_IC12 74.24742 1.5986018
## IC03_IC13 -69.63924 3.1708220
## IC04_IC12 50.24836 0.7792706
## IC05_IC06 142.90292 -2.6916133
## IC07_IC13 -31.65097 -3.0726614
## IC08_IC13 -79.81150 3.1340408
## IC08_IC18 -25.90024 2.6504559
## IC12_IC17 76.46526 3.1698860
## IC13_IC14 -184.01867 -1.4323553
## IC14_IC20 -192.72671 1.3583569
## IC17_IC18 73.96681 1.9481312
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC17 0.008763035 -0.3750485
## IC03_IC12 0.111692696 -0.2424582
## IC03_IC13 0.001791788 -0.4774934
## IC04_IC12 0.436861014 -0.1159924
## IC05_IC06 0.007792825 0.4094693
## IC07_IC13 0.002457157 0.4840844
## IC08_IC13 0.002018630 -0.4771050
## IC08_IC18 0.008766823 -0.4048714
## IC12_IC17 0.001797255 -0.4434389
## IC13_IC14 0.153805828 0.2421429
## IC14_IC20 0.176079226 -0.1886743
## IC17_IC18 0.052980770 -0.2765940
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC17 30.651666 49.84265 6.2097829
## IC03_IC12 16.732425 35.92341 2.3751428
## IC03_IC13 -140.456801 -121.26582 94.2481540
## IC04_IC12 37.063246 56.25423 0.6026362
## IC05_IC06 118.941330 138.13231 3.8455586
## IC07_IC13 -78.177035 -58.98605 44.3964724
## IC08_IC13 -100.046930 -80.85595 14.1882320
## IC08_IC18 -115.995021 -96.80404 7.7427657
## IC12_IC17 5.589369 24.78035 82.9404238
## IC13_IC14 -202.934458 -183.74348 1.4573599
## IC14_IC20 -271.391838 -252.20086 1.5981694
## IC17_IC18 36.767238 55.95822 3.5793761
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC17 0.745580918 0.4574032
## IC03_IC12 0.129251168 0.8973790
## IC03_IC13 -1.430886619 0.1551057
## IC04_IC12 -0.636052022 0.5259743
## IC05_IC06 -1.142312387 0.2556368
## IC07_IC13 -0.628783940 0.5307058
## IC08_IC13 0.513002368 0.6089087
## IC08_IC18 -1.102566731 0.2724590
## IC12_IC17 -1.329752480 0.1861646
## IC13_IC14 0.637411946 0.5250914
## IC14_IC20 -0.867535821 0.3874093
## IC17_IC18 -0.009049277 0.9927951
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC17 -0.13407841 86.72773
## IC03_IC12 -0.02224662 48.64798
## IC03_IC13 0.23996196 -41.27593
## IC04_IC12 0.10810205 65.50228
## IC05_IC06 0.19993686 108.61103
## IC07_IC13 0.14332576 -18.34445
## IC08_IC13 -0.10618911 -42.84859
## IC08_IC18 0.19085529 -30.98909
## IC12_IC17 0.24546514 55.09476
## IC13_IC14 -0.13878095 -163.86169
## IC14_IC20 0.16231789 -86.55234
## IC17_IC18 -0.01201644 11.24587
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC17 103.502470 -0.06145934
## IC03_IC12 65.422719 0.68677424
## IC03_IC13 -24.501191 -1.41380815
## IC04_IC12 82.277027 1.50373894
## IC05_IC06 125.385776 -0.02632118
## IC07_IC13 -1.569709 0.11377506
## IC08_IC13 -26.073843 -2.28934563
## IC08_IC18 -14.214344 -2.97670556
## IC12_IC17 71.869502 -0.95627517
## IC13_IC14 -147.086949 -1.08564086
## IC14_IC20 -69.777594 1.25149443
## IC17_IC18 28.020611 0.56274395
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC17 0.951093149 0.009856602
## IC03_IC12 0.493517753 -0.109585142
## IC03_IC13 0.159944262 0.229436077
## IC04_IC12 0.135211948 -0.265279021
## IC05_IC06 0.979043819 0.028987931
## IC07_IC13 0.909601612 -0.026449777
## IC08_IC13 0.023764128 0.400753332
## IC08_IC18 0.003509457 0.577323507
## IC12_IC17 0.340808976 0.157209625
## IC13_IC14 0.279761069 0.244389150
## IC14_IC20 0.213129071 -0.247627605
## IC17_IC18 0.574633528 -0.088110873
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC17 36.20507 53.222764
## IC03_IC12 63.30585 80.323540
## IC03_IC13 -75.14364 -58.125945
## IC04_IC12 46.57862 63.596313
## IC05_IC06 108.75878 125.776468
## IC07_IC13 -21.93822 -4.920526
## IC08_IC13 -37.22171 -20.204020
## IC08_IC18 -41.80025 -24.782563
## IC12_IC17 45.35860 62.376294
## IC13_IC14 -147.68513 -130.667440
## IC14_IC20 -111.91215 -94.894460
## IC17_IC18 45.43893 62.456623
## SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC17 0.5938849
## IC03_IC12 0.8222121
## IC03_IC13 1.8938805
## IC04_IC12 0.6964103
## IC05_IC06 0.5096203
## IC07_IC13 0.6128578
## IC08_IC13 1.3497890
## IC08_IC18 22.6621292
## IC12_IC17 1.0623730
## IC13_IC14 0.6032523
## IC14_IC20 0.5002514
## IC17_IC18 0.7571995
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC17 IC01_IC17 13.6829890 6.2097829
## IC03_IC12 IC03_IC12 11.3486699 2.3751428
## IC03_IC13 IC03_IC13 1.9106092 94.2481540
## IC04_IC12 IC04_IC12 11.5035145 0.6026362
## IC05_IC06 IC05_IC06 26.2974372 3.8455586
## IC07_IC13 IC07_IC13 123.1232890 44.3964724
## IC08_IC13 IC08_IC13 0.5539346 14.1882320
## IC08_IC18 IC08_IC18 1.5428038 7.7427657
## IC12_IC17 IC12_IC17 1.9229706 82.9404238
## IC13_IC14 IC13_IC14 64.1224204 1.4573599
## IC14_IC20 IC14_IC20 11.9205026 1.5981694
## IC17_IC18 IC17_IC18 13.4117521 3.5793761
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 0.9
# Z threshold
z_thresh = 0.9
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC17 IC01_IC17 1.1940808 0.233991855
## IC03_IC12 IC03_IC12 2.6603891 0.008498259
## IC03_IC13 IC03_IC13 2.1694654 0.031336708
## IC04_IC12 IC04_IC12 1.7519463 0.081458088
## IC05_IC06 IC05_IC06 -1.4594704 0.146150104
## IC07_IC13 IC07_IC13 -2.4452150 0.015423228
## IC12_IC17 IC12_IC17 0.8543305 0.394038903
## IC13_IC14 IC13_IC14 -1.9232790 0.055997264
## IC17_IC18 IC17_IC18 3.1200405 0.002101385
## IC18_IC19 IC18_IC19 -0.5166293 0.606038716
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC17 -0.19649856 52.15723
## IC03_IC12 -0.38448867 27.18358
## IC03_IC13 -0.32427108 -78.16961
## IC04_IC12 -0.26806180 17.72636
## IC05_IC06 0.21443563 152.69424
## IC07_IC13 0.34744877 -55.36740
## IC12_IC17 -0.12711967 63.21058
## IC13_IC14 0.27069103 -234.18404
## IC17_IC18 -0.45582958 56.43592
## IC18_IC19 0.09370915 -63.73547
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC17 71.54388 2.226675
## IC03_IC12 46.57024 2.476757
## IC03_IC13 -58.78296 2.032160
## IC04_IC12 37.11301 2.837115
## IC05_IC06 172.08089 -3.332082
## IC07_IC13 -35.98075 -3.574820
## IC12_IC17 82.59723 1.979163
## IC13_IC14 -214.79739 -3.465509
## IC17_IC18 75.82257 2.675990
## IC18_IC19 -44.34882 2.216665
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC17 0.0271311934 -0.3126138
## IC03_IC12 0.0141229781 -0.3604296
## IC03_IC13 0.0435149857 -0.3146501
## IC04_IC12 0.0050411087 -0.4199090
## IC05_IC06 0.0010339977 0.4741883
## IC07_IC13 0.0004433505 0.5084838
## IC12_IC17 0.0492269786 -0.2869355
## IC13_IC14 0.0006528278 0.4889647
## IC17_IC18 0.0080947826 -0.3811268
## IC18_IC19 0.0278199145 -0.3131645
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC17 21.29519 40.963880 6.394467
## IC03_IC12 31.70846 51.377147 14.102934
## IC03_IC13 -163.99950 -144.330817 5.332813
## IC04_IC12 31.88404 51.552728 28.344443
## IC05_IC06 125.38211 145.050800 71.430424
## IC07_IC13 -122.31427 -102.645578 267.606852
## IC12_IC17 34.91776 54.586447 3.643369
## IC13_IC14 -226.99247 -207.323782 143.740693
## IC17_IC18 34.87671 54.545398 21.942334
## IC18_IC19 -12.12802 7.540671 1.286343
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC17 0.3694011 0.712293963
## IC03_IC12 1.8085136 0.072314838
## IC03_IC13 2.8415347 0.005045815
## IC04_IC12 1.7574970 0.080654558
## IC05_IC06 0.4069436 0.684567405
## IC07_IC13 -1.6732648 0.096136180
## IC12_IC17 2.4172479 0.016708367
## IC13_IC14 -2.4827350 0.014019785
## IC17_IC18 2.8727582 0.004594296
## IC18_IC19 1.6028123 0.110854587
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC17 -0.05016234 84.32660
## IC03_IC12 -0.28928800 58.27217
## IC03_IC13 -0.44277156 -106.84779
## IC04_IC12 -0.29469890 29.45273
## IC05_IC06 -0.06328016 121.77654
## IC07_IC13 0.24206035 -47.07759
## IC12_IC17 -0.39364722 59.05240
## IC13_IC14 0.39836120 -193.50246
## IC17_IC18 -0.46048779 55.12351
## IC18_IC19 -0.26529743 -90.29840
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC17 103.21157 3.0246998
## IC03_IC12 77.15713 1.4781494
## IC03_IC13 -87.96282 2.7715486
## IC04_IC12 48.33770 0.2568189
## IC05_IC06 140.66150 -2.2478960
## IC07_IC13 -28.19262 -2.7046353
## IC12_IC17 77.93737 2.7360647
## IC13_IC14 -174.61749 -0.8767594
## IC17_IC18 74.00848 1.5520366
## IC18_IC19 -71.41343 2.4794661
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC17 0.002882340 -0.47748080
## IC03_IC12 0.141251361 -0.22945843
## IC03_IC13 0.006212221 -0.42581967
## IC04_IC12 0.797634723 -0.04730255
## IC05_IC06 0.025891461 0.36354360
## IC07_IC13 0.007546534 0.46198324
## IC12_IC17 0.006890468 -0.40829514
## IC13_IC14 0.381876802 0.18210254
## IC17_IC18 0.122546521 -0.23240787
## IC18_IC19 0.014150239 -0.40553482
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC17 21.02652 39.87650 10.6085758
## IC03_IC12 11.82683 30.67681 2.0659636
## IC03_IC13 -126.23462 -107.38464 30.8172024
## IC04_IC12 41.33343 60.18341 0.4225573
## IC05_IC06 115.14269 133.99267 1.4785356
## IC07_IC13 -71.36100 -52.51102 19.5978233
## IC12_IC17 10.75061 29.60059 27.2602919
## IC13_IC14 -187.80986 -168.95988 0.5582649
## IC17_IC18 39.88986 58.73984 1.5571320
## IC18_IC19 -53.64883 -34.79885 12.0715553
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC17 0.66401571 0.50797563
## IC03_IC12 0.36326262 0.71705866
## IC03_IC13 -0.50468222 0.61472323
## IC04_IC12 -0.22444565 0.82279873
## IC05_IC06 -1.56335434 0.12064724
## IC07_IC13 -0.39972117 0.69008451
## IC12_IC17 -1.44404066 0.15137743
## IC13_IC14 0.83133199 0.40746518
## IC17_IC18 -0.09780341 0.92225433
## IC18_IC19 -1.72330241 0.08745318
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC17 -0.120233727 86.830643
## IC03_IC12 -0.065028394 48.521206
## IC03_IC13 0.074734503 -39.535261
## IC04_IC12 0.031998054 65.840061
## IC05_IC06 0.280547796 107.477618
## IC07_IC13 0.100688957 -18.125858
## IC12_IC17 0.264861665 54.771130
## IC13_IC14 -0.166130409 -164.159921
## IC17_IC18 0.004134189 11.221942
## IC18_IC19 0.311698524 -9.869156
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC17 103.605386 -0.90741559
## IC03_IC12 65.295950 0.80409337
## IC03_IC13 -22.760518 -0.76915957
## IC04_IC12 82.614805 1.95474606
## IC05_IC06 124.252361 -0.49596276
## IC07_IC13 -1.351114 -0.01792758
## IC12_IC17 71.545873 -0.59776809
## IC13_IC14 -147.385178 -1.36627941
## IC17_IC18 27.996685 0.76908380
## IC18_IC19 6.905587 -0.25646378
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC17 0.36594714 0.155535831
## IC03_IC12 0.42288192 -0.129230992
## IC03_IC13 0.44326120 0.123236336
## IC04_IC12 0.05286326 -0.354222483
## IC05_IC06 0.62079862 0.103887456
## IC07_IC13 0.98572544 0.004982477
## IC12_IC17 0.55108483 0.090009293
## IC13_IC14 0.17432387 0.322753186
## IC17_IC18 0.44330600 -0.139519833
## IC18_IC19 0.79801776 0.034629963
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC17 34.32432 51.389443
## IC03_IC12 63.28111 80.346231
## IC03_IC13 -75.71992 -58.654801
## IC04_IC12 45.34090 62.406024
## IC05_IC06 108.27899 125.344108
## IC07_IC13 -23.33855 -6.273428
## IC12_IC17 44.93623 62.001355
## IC13_IC14 -150.70409 -133.638970
## IC17_IC18 44.28790 61.353024
## IC18_IC19 37.76692 54.832046
## SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC17 0.5717674
## IC03_IC12 0.9268155
## IC03_IC13 0.9293109
## IC04_IC12 1.4463475
## IC05_IC06 0.5934180
## IC07_IC13 0.6769633
## IC12_IC17 0.6991607
## IC13_IC14 0.5358728
## IC17_IC18 0.7845247
## IC18_IC19 0.4220160
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC17 IC01_IC17 6.394467 10.6085758
## IC03_IC12 IC03_IC12 14.102934 2.0659636
## IC03_IC13 IC03_IC13 5.332813 30.8172024
## IC04_IC12 IC04_IC12 28.344443 0.4225573
## IC05_IC06 IC05_IC06 71.430424 1.4785356
## IC07_IC13 IC07_IC13 267.606852 19.5978233
## IC12_IC17 IC12_IC17 3.643369 27.2602919
## IC13_IC14 IC13_IC14 143.740693 0.5582649
## IC17_IC18 IC17_IC18 21.942334 1.5571320
## IC18_IC19 IC18_IC19 1.286343 12.0715553
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 1
# Z threshold
z_thresh = 1
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
#------------------------------------------------------------------------------
# tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df = subset(tmp_df,tmp_df$z_ds_group!="RRB_over_SC")
#------------------------------------------------------------------------------
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
tmp_df$AB_pct_severity = tmp_df$A_pct_severity + tmp_df$B_pct_severity
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","AB_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC",
"SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval",
"SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC",
"SCequalRRB_vs_SCoverRRB.repBF",
"SCcorr_Disc.r","SCcorr_Disc.t","SCcorr_Disc.pval",
"SCcorr_Rep.r","SCcorr_Rep.t","SCcorr_Rep.pval","SCcorr.repBF",
"RRBcorr_Disc.r","RRBcorr_Disc.t","RRBcorr_Disc.pval",
"RRBcorr_Rep.r","RRBcorr_Rep.t","RRBcorr_Rep.pval","RRBcorr.repBF",
"SumSCRRB_Disc.r","SumSCRRB_Disc.t","SumSCRRB_Disc.pval",
"SumSCRRB_Rep.r","SumSCRRB_Rep.t","SumSCRRB_Rep.pval","SumSCRRB.repBF",
"zds_Disc.r","zds_Disc.t","zds_Disc.pval",
"zds_Rep.r","zds_Rep.t","zds_Rep.pval","zds.repBF",
"SumSCRRB_SCequalRRB_Disc.r","SumSCRRB_SCequalRRB_Disc.t","SumSCRRB_SCequalRRB_Disc.pval",
"SumSCRRB_SCequalRRB_Rep.r","SumSCRRB_SCequalRRB_Rep.t",
"SumSCRRB_SCequalRRB_Rep.pval","SumSCRRB_SCequalRRB.repBF",
"zds_SCequalRRB_Disc.r","zds_SCequalRRB_Disc.t","zds_SCequalRRB_Disc.pval",
"zds_SCequalRRB_Rep.r","zds_SCequalRRB_Rep.t","zds_SCequalRRB_Rep.pval","zds_SCequalRRB.repBF",
"zds_SCoverRRB_Disc.r","zds_SCoverRRB_Disc.t","zds_SCoverRRB_Disc.pval",
"zds_SCoverRRB_Rep.r","zds_SCoverRRB_Rep.t","zds_SCoverRRB_Rep.pval","zds_SCoverRRB.repBF",
"VinelandABC_Disc.r","VinelandABC_Disc.t","VinelandABC_Disc.pval",
"VinelandABC_Rep.r","VinelandABC_Rep.t",
"VinelandABC_Rep.pval","VinelandABC.repBF",
"VinelandABC_SCequalRRB_Disc.r","VinelandABC_SCequalRRB_Disc.t","VinelandABC_SCequalRRB_Disc.pval",
"VinelandABC_SCequalRRB_Rep.r","VinelandABC_SCequalRRB_Rep.t",
"VinelandABC_SCequalRRB_Rep.pval","VinelandABC_SCequalRRB.repBF",
"VinelandABC_SCoverRRB_Disc.r","VinelandABC_SCoverRRB_Disc.t","VinelandABC_SCoverRRB_Disc.pval",
"VinelandABC_SCoverRRB_Rep.r","VinelandABC_SCoverRRB_Rep.t",
"VinelandABC_SCoverRRB_Rep.pval","VinelandABC_SCoverRRB.repBF")
# "vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard"
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
DASD = subset(asd_df, asd_df$dataset=="Discovery")
DASD$site = factor(DASD$site)
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"A_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SCcorr_Disc.t"] = res$tTable["A_pct_severity","t-value"]
aovres[y_var,"SCcorr_Disc.pval"] = res$tTable["A_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"A_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ A_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$A_pct_severity)
aovres[y_var,"SCcorr_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"B_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"RRBcorr_Disc.t"] = res$tTable["B_pct_severity","t-value"]
aovres[y_var,"RRBcorr_Disc.pval"] = res$tTable["B_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"B_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ B_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$B_pct_severity)
aovres[y_var,"RRBcorr_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SumSCRRB_Disc.t"] = res$tTable["AB_pct_severity","t-value"]
aovres[y_var,"SumSCRRB_Disc.pval"] = res$tTable["AB_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$AB_pct_severity)
aovres[y_var,"SumSCRRB_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_Disc.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_Disc.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$z_ds)
aovres[y_var,"zds_Disc.r"] = res$estimate
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$vabsabcabc_standard)
aovres[y_var,"VinelandABC_Disc.r"] = res$estimate
DASD_SCequalRRB = subset(DASD,DASD$subgrp=="SC_equal_RRB")
DASD_SCoverRRB = subset(DASD,DASD$subgrp=="SC_over_RRB")
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_SCequalRRB_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_SCequalRRB_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$vabsabcabc_standard)
aovres[y_var,"VinelandABC_SCequalRRB_Disc.r"] = res$estimate
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCoverRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_SCoverRRB_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_SCoverRRB_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCoverRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$vabsabcabc_standard)
aovres[y_var,"VinelandABC_SCoverRRB_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SumSCRRB_SCequalRRB_Disc.t"] = res$tTable["AB_pct_severity","t-value"]
aovres[y_var,"SumSCRRB_SCequalRRB_Disc.pval"] = res$tTable["AB_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$AB_pct_severity)
aovres[y_var,"SumSCRRB_SCequalRRB_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_SCequalRRB_Disc.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_SCequalRRB_Disc.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$z_ds)
aovres[y_var,"zds_SCequalRRB_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCoverRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_SCoverRRB_Disc.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_SCoverRRB_Disc.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCoverRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$z_ds)
aovres[y_var,"zds_SCoverRRB_Disc.r"] = res$estimate
# res = cor.test(DASD[,"A_pct_severity"],DASD[,y_var])
# aovres[y_var,"SCcorr_Disc.r"] = res$estimate
# aovres[y_var,"SCcorr_Disc.pval"] = res$p.value
# res = cor.test(DASD[,"B_pct_severity"],DASD[,y_var])
# aovres[y_var,"RRBcorr_Disc.r"] = res$estimate
# aovres[y_var,"RRBcorr_Disc.pval"] = res$p.value
# res = cor.test(DASD[,"AB_pct_severity"],DASD[,y_var])
# aovres[y_var,"SumSCRRB_Disc.r"] = res$estimate
# aovres[y_var,"SumSCRRB_Disc.pval"] = res$p.value
n_orig = dim(DASD)[1]
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
#--------------------------------------------------------------------------
DASD = subset(asd_df, asd_df$dataset=="Replication")
DASD$site = factor(DASD$site)
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"A_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SCcorr_Rep.t"] = res$tTable["A_pct_severity","t-value"]
aovres[y_var,"SCcorr_Rep.pval"] = res$tTable["A_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"A_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ A_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$A_pct_severity)
aovres[y_var,"SCcorr_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"B_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"RRBcorr_Rep.t"] = res$tTable["B_pct_severity","t-value"]
aovres[y_var,"RRBcorr_Rep.pval"] = res$tTable["B_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"B_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ B_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$B_pct_severity)
aovres[y_var,"RRBcorr_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SumSCRRB_Rep.t"] = res$tTable["AB_pct_severity","t-value"]
aovres[y_var,"SumSCRRB_Rep.pval"] = res$tTable["AB_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$AB_pct_severity)
aovres[y_var,"SumSCRRB_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_Rep.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_Rep.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$z_ds)
aovres[y_var,"zds_Rep.r"] = res$estimate
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$vabsabcabc_standard)
aovres[y_var,"VinelandABC_Rep.r"] = res$estimate
DASD_SCequalRRB = subset(DASD,DASD$subgrp=="SC_equal_RRB")
DASD_SCoverRRB = subset(DASD,DASD$subgrp=="SC_over_RRB")
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_SCequalRRB_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_SCequalRRB_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$vabsabcabc_standard)
aovres[y_var,"VinelandABC_SCequalRRB_Rep.r"] = res$estimate
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCoverRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_SCoverRRB_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_SCoverRRB_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCoverRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$vabsabcabc_standard)
aovres[y_var,"VinelandABC_SCoverRRB_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SumSCRRB_SCequalRRB_Rep.t"] = res$tTable["AB_pct_severity","t-value"]
aovres[y_var,"SumSCRRB_SCequalRRB_Rep.pval"] = res$tTable["AB_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$AB_pct_severity)
aovres[y_var,"SumSCRRB_SCequalRRB_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_SCequalRRB_Rep.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_SCequalRRB_Rep.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$z_ds)
aovres[y_var,"zds_SCequalRRB_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCoverRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_SCoverRRB_Rep.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_SCoverRRB_Rep.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCoverRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$z_ds)
aovres[y_var,"zds_SCoverRRB_Rep.r"] = res$estimate
# res = cor.test(DASD[,"A_pct_severity"],DASD[,y_var])
# aovres[y_var,"SCcorr_Rep.r"] = res$estimate
# aovres[y_var,"SCcorr_Rep.pval"] = res$p.value
# res = cor.test(DASD[,"B_pct_severity"],DASD[,y_var])
# aovres[y_var,"RRBcorr_Rep.r"] = res$estimate
# aovres[y_var,"RRBcorr_Rep.pval"] = res$p.value
# res = cor.test(DASD[,"AB_pct_severity"],DASD[,y_var])
# aovres[y_var,"SumSCRRB_Rep.r"] = res$estimate
# aovres[y_var,"SumSCRRB_Rep.pval"] = res$p.value
n_rep = dim(DASD)[1]
#--------------------------------------------------------------------------
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
#--------------------------------------------------------------------------
res_bf = BFSALL(tobs =aovres[y_var,"SCcorr_Disc.t"],
trep = aovres[y_var,"SCcorr_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"SCcorr.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"RRBcorr_Disc.t"],
trep = aovres[y_var,"RRBcorr_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"RRBcorr.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"SumSCRRB_Disc.t"],
trep = aovres[y_var,"SumSCRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"SumSCRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"zds_Disc.t"],
trep = aovres[y_var,"zds_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"zds.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_Disc.t"],
trep = aovres[y_var,"VinelandABC_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"VinelandABC.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"SumSCRRB_SCequalRRB_Disc.t"],
trep = aovres[y_var,"SumSCRRB_SCequalRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"SumSCRRB_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"zds_SCequalRRB_Disc.t"],
trep = aovres[y_var,"zds_SCequalRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"zds_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"zds_SCoverRRB_Disc.t"],
trep = aovres[y_var,"zds_SCoverRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"zds_SCoverRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_SCequalRRB_Disc.t"],
trep = aovres[y_var,"VinelandABC_SCequalRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"VinelandABC_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_SCoverRRB_Disc.t"],
trep = aovres[y_var,"VinelandABC_SCoverRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"VinelandABC_SCoverRRB.repBF"] = res_bf["Replication BF","Replication 1"]
# res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"SCcorr_Disc.r"],
# n.orig = n_orig,
# r.rep = aovres[y_var,"SCcorr_Rep.r"],
# n.rep = n_rep)
# aovres[y_var,"SCcorr.repBF"] = res_bf["BF10"]
# res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"RRBcorr_Disc.r"],
# n.orig = n_orig,
# r.rep = aovres[y_var,"RRBcorr_Rep.r"],
# n.rep = n_rep)
# aovres[y_var,"RRBcorr.repBF"] = res_bf["BF10"]
# res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"SumSCRRB_Disc.r"],
# n.orig = n_orig,
# r.rep = aovres[y_var,"SumSCRRB_Rep.r"],
# n.rep = n_rep)
# aovres[y_var,"SumSCRRB.repBF"] = res_bf["BF10"]
#--------------------------------------------------------------------------
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
# write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask5 = aovres$SCcorr.repBF>=10
mask6 = aovres$RRBcorr.repBF>=10
mask7 = aovres$SumSCRRB.repBF>=10
mask8 = aovres$zds.repBF>=10
mask9 = aovres$zds_SCequalRRB.repBF>=10
mask10 = aovres$zds_SCoverRRB.repBF>=10
mask11 = aovres$SumSCRRB_SCequalRRB.repBF>=10
mask12 = aovres$VinelandABC.repBF>=10
mask13 = aovres$VinelandABC_SCequalRRB.repBF>=10
mask14 = aovres$VinelandABC_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 | mask5 | mask6 | mask7 | mask8 | mask9 | mask10 | mask11 | mask12 | mask13 | mask14
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC12 IC01_IC12 -0.12932999 0.8972333475
## IC03_IC12 IC03_IC12 2.94092443 0.0036790286
## IC03_IC13 IC03_IC13 2.15434545 0.0324720970
## IC03_IC18 IC03_IC18 0.92697519 0.3551154150
## IC04_IC06 IC04_IC06 0.63678759 0.5250296751
## IC04_IC11 IC04_IC11 -0.02928825 0.9766654383
## IC04_IC12 IC04_IC12 1.51303104 0.1319333691
## IC05_IC06 IC05_IC06 -1.32603675 0.1864194275
## IC05_IC19 IC05_IC19 1.07979275 0.2816032599
## IC07_IC13 IC07_IC13 -2.65749362 0.0085427061
## IC08_IC11 IC08_IC11 -0.10964393 0.9128074859
## IC08_IC20 IC08_IC20 0.01351206 0.9892334426
## IC11_IC12 IC11_IC12 1.30109409 0.1948018611
## IC12_IC17 IC12_IC17 0.90500594 0.3666083763
## IC12_IC20 IC12_IC20 -0.01609537 0.9871751958
## IC13_IC14 IC13_IC14 -2.09077840 0.0378780563
## IC14_IC16 IC14_IC16 -2.85242249 0.0048192212
## IC14_IC18 IC14_IC18 -0.03279725 0.9738707030
## IC14_IC20 IC14_IC20 1.18219798 0.2386045361
## IC15_IC17 IC15_IC17 -1.07416328 0.2841117683
## IC17_IC18 IC17_IC18 3.39889683 0.0008243562
## IC18_IC19 IC18_IC19 -0.20919572 0.8345195712
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC12 0.015215795 1.009903
## IC03_IC12 -0.411972548 26.290817
## IC03_IC13 -0.311312998 -85.783794
## IC03_IC18 -0.129913758 -69.802261
## IC04_IC06 -0.066412895 223.253963
## IC04_IC11 0.003052759 191.732718
## IC04_IC12 -0.224794950 17.212437
## IC05_IC06 0.188637115 152.987250
## IC05_IC19 -0.153508670 137.029142
## IC07_IC13 0.363200583 -61.276004
## IC08_IC11 0.006170505 107.690133
## IC08_IC20 0.010941672 -113.031032
## IC11_IC12 -0.192876882 14.349573
## IC12_IC17 -0.132893318 60.262572
## IC12_IC20 0.000814278 -97.451328
## IC13_IC14 0.284849007 -246.656905
## IC14_IC16 0.405672732 -226.569219
## IC14_IC18 -0.013013514 -235.729572
## IC14_IC20 -0.172473911 -206.710456
## IC15_IC17 0.164531406 37.129649
## IC17_IC18 -0.485237543 53.078585
## IC18_IC19 0.045677851 -69.358310
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC12 20.61705 -0.4903790
## IC03_IC12 45.89797 2.5559765
## IC03_IC13 -66.17664 2.3885252
## IC03_IC18 -50.19511 0.2244515
## IC04_IC06 242.86111 0.1487822
## IC04_IC11 211.33987 0.5984408
## IC04_IC12 36.81959 2.6386990
## IC05_IC06 172.59440 -3.4567910
## IC05_IC19 156.63629 0.7698455
## IC07_IC13 -41.66885 -3.6712914
## IC08_IC11 127.29728 0.7814798
## IC08_IC20 -93.42388 1.6128900
## IC11_IC12 33.95672 0.5961633
## IC12_IC17 79.86972 1.8892709
## IC12_IC20 -77.84418 -0.5697457
## IC13_IC14 -227.04976 -3.5571004
## IC14_IC16 -206.96207 -2.4258463
## IC14_IC18 -216.12242 -1.5180237
## IC14_IC20 -187.10331 2.4606965
## IC15_IC17 56.73680 0.3329474
## IC17_IC18 72.68573 2.5402038
## IC18_IC19 -49.75116 1.9914692
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC12 0.6244114305 0.06059042
## IC03_IC12 0.0113429584 -0.36335796
## IC03_IC13 0.0178604678 -0.36514527
## IC03_IC18 0.8226386330 -0.03007772
## IC04_IC06 0.8818776982 -0.01843875
## IC04_IC11 0.5502333170 -0.08377147
## IC04_IC12 0.0089879596 -0.38545623
## IC05_IC06 0.0006696122 0.48303797
## IC05_IC19 0.4423136691 -0.10328839
## IC07_IC13 0.0003107532 0.51271271
## IC08_IC11 0.4354585223 -0.11556160
## IC08_IC20 0.1083691682 -0.23129206
## IC11_IC12 0.5517504974 -0.09921851
## IC12_IC17 0.0603244841 -0.26928952
## IC12_IC20 0.5694993271 0.08074945
## IC13_IC14 0.0004696920 0.49128413
## IC14_IC16 0.0161737397 0.32618819
## IC14_IC18 0.1306115703 0.20644564
## IC14_IC20 0.0147276617 -0.33189896
## IC15_IC17 0.7395280473 -0.04972028
## IC17_IC18 0.0118500978 -0.35626511
## IC18_IC19 0.0478109107 -0.27512176
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC12 -26.373181 -6.553351 0.7636743
## IC03_IC12 28.435303 48.255132 16.6928467
## IC03_IC13 -167.987023 -148.167194 11.6387970
## IC03_IC18 -27.342115 -7.522285 0.6298486
## IC04_IC06 219.555252 239.375081 0.6636211
## IC04_IC11 210.049655 229.869485 0.7585735
## IC04_IC12 28.932842 48.752671 16.2382806
## IC05_IC06 124.867452 144.687282 81.9293437
## IC05_IC19 125.539779 145.359608 0.9141969
## IC07_IC13 -117.659303 -97.839473 390.8687559
## IC08_IC11 74.236654 94.056483 0.7797743
## IC08_IC20 -90.644027 -70.824198 1.3733856
## IC11_IC12 -66.035966 -46.216136 0.7316592
## IC12_IC17 35.550528 55.370357 3.2894374
## IC12_IC20 -154.381457 -134.561628 0.7625460
## IC13_IC14 -235.786244 -215.966414 203.6196044
## IC14_IC16 -225.359769 -205.539939 12.0374592
## IC14_IC18 -206.117344 -186.297515 1.2864757
## IC14_IC20 -213.770772 -193.950943 9.5636043
## IC15_IC17 82.115061 101.934891 0.4468222
## IC17_IC18 34.642907 54.462737 13.6220191
## IC18_IC19 -8.440352 11.379477 1.5334740
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC12 -1.6016220 0.111175445
## IC03_IC12 1.6311594 0.104787333
## IC03_IC13 3.0384977 0.002770001
## IC03_IC18 1.3586932 0.176121067
## IC04_IC06 0.7556826 0.450930176
## IC04_IC11 -0.3284564 0.742988194
## IC04_IC12 2.0471761 0.042246225
## IC05_IC06 0.3439435 0.731332049
## IC05_IC19 0.7223886 0.471090744
## IC07_IC13 -1.6341733 0.104152376
## IC08_IC11 -1.0622516 0.289692763
## IC08_IC20 0.2430750 0.808252912
## IC11_IC12 1.4249315 0.156088924
## IC12_IC17 2.4057786 0.017257297
## IC12_IC20 -0.4161891 0.677819095
## IC13_IC14 -2.5232655 0.012584513
## IC14_IC16 -2.5004840 0.013390659
## IC14_IC18 -1.5732890 0.117590939
## IC14_IC20 1.5217336 0.130013414
## IC15_IC17 -1.5866893 0.114521033
## IC17_IC18 2.7750025 0.006165397
## IC18_IC19 1.4322503 0.153987109
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC12 0.24713844 -11.31508
## IC03_IC12 -0.27052920 58.10547
## IC03_IC13 -0.50222278 -100.64322
## IC03_IC18 -0.22163203 -39.68638
## IC04_IC06 -0.13799203 203.69961
## IC04_IC11 0.04647632 177.96376
## IC04_IC12 -0.35563285 29.53062
## IC05_IC06 -0.05147755 122.11045
## IC05_IC19 -0.11813608 112.95914
## IC07_IC13 0.24540684 -42.05848
## IC08_IC11 0.19185902 104.84243
## IC08_IC20 -0.04489083 -58.54158
## IC11_IC12 -0.24106889 19.15637
## IC12_IC17 -0.40744891 60.84566
## IC12_IC20 0.07097610 -88.48662
## IC13_IC14 0.42484206 -185.12389
## IC14_IC16 0.41686447 -212.78658
## IC14_IC18 0.29484340 -206.51828
## IC14_IC20 -0.27242468 -190.99132
## IC15_IC17 0.27735513 29.37056
## IC17_IC18 -0.45581912 58.39327
## IC18_IC19 -0.25228370 -87.23892
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC12 7.392878 -0.3112332
## IC03_IC12 76.813438 1.3715598
## IC03_IC13 -81.935259 2.3499277
## IC03_IC18 -20.978417 1.0375948
## IC04_IC06 222.407570 -0.9048711
## IC04_IC11 196.671727 0.7716249
## IC04_IC12 48.238581 0.3561788
## IC05_IC06 140.818415 -2.1415401
## IC05_IC19 131.667099 1.3314022
## IC07_IC13 -23.350517 -2.7094089
## IC08_IC11 123.550392 -0.4828727
## IC08_IC20 -39.833614 0.1755624
## IC11_IC12 37.864336 2.4432320
## IC12_IC17 79.553620 2.6815515
## IC12_IC20 -69.778657 -1.2062072
## IC13_IC14 -166.415931 -0.8444977
## IC14_IC16 -194.078621 -1.6690478
## IC14_IC18 -187.810316 -1.1928435
## IC14_IC20 -172.283360 1.7499557
## IC15_IC17 48.078520 0.2161937
## IC17_IC18 77.101236 1.5816738
## IC18_IC19 -68.530956 2.5214903
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC12 0.756021035 0.054571883
## IC03_IC12 0.172085561 -0.218804283
## IC03_IC13 0.019974826 -0.381536323
## IC03_IC18 0.300995495 -0.129538403
## IC04_IC06 0.366869175 0.181673077
## IC04_IC11 0.441453856 -0.156300658
## IC04_IC12 0.722167223 -0.066300183
## IC05_IC06 0.033716159 0.361054596
## IC05_IC19 0.184915728 -0.230584683
## IC07_IC13 0.007461023 0.470994388
## IC08_IC11 0.629833996 0.082135168
## IC08_IC20 0.860855660 0.025908126
## IC11_IC12 0.015623393 -0.387366952
## IC12_IC17 0.008082279 -0.417081872
## IC12_IC20 0.229485457 0.200067928
## IC13_IC14 0.399629134 0.184463431
## IC14_IC16 0.097027480 0.309502931
## IC14_IC18 0.234664128 0.199754146
## IC14_IC20 0.082007723 -0.269827359
## IC15_IC17 0.829107096 0.001739074
## IC17_IC18 0.115662520 -0.240649235
## IC18_IC19 0.012645736 -0.417388548
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC12 -23.483627 -4.775664 0.4923188
## IC03_IC12 14.336916 33.044879 1.7845001
## IC03_IC13 -122.920546 -104.212583 9.6795040
## IC03_IC18 -37.540287 -18.832325 1.1861277
## IC04_IC06 188.362149 207.070112 0.5358113
## IC04_IC11 208.410372 227.118335 0.7054349
## IC04_IC12 44.412064 63.120027 0.3722300
## IC05_IC06 115.376603 134.084566 1.4657777
## IC05_IC19 116.075315 134.783278 1.5631620
## IC07_IC13 -74.701619 -55.993656 19.4748090
## IC08_IC11 68.799714 87.507677 0.7331277
## IC08_IC20 -96.472495 -77.764532 0.7193256
## IC11_IC12 -52.391343 -33.683380 10.3433042
## IC12_IC17 9.536299 28.244262 23.7664845
## IC12_IC20 -125.750136 -107.042173 1.2533726
## IC13_IC14 -179.360228 -160.652265 0.5068066
## IC14_IC16 -167.612815 -148.904852 2.3981289
## IC14_IC18 -189.475459 -170.767496 1.3938615
## IC14_IC20 -269.871770 -251.163807 3.2059464
## IC15_IC17 67.299090 86.007052 0.3242601
## IC17_IC18 39.641534 58.349496 1.7467207
## IC18_IC19 -53.158157 -34.450194 12.0366791
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC12 1.19563976 0.2341945
## IC03_IC12 0.57327322 0.5675325
## IC03_IC13 -0.94945435 0.3442978
## IC03_IC18 -0.68757172 0.4930493
## IC04_IC06 -0.45375807 0.6508225
## IC04_IC11 0.09066694 0.9279084
## IC04_IC12 -0.73436129 0.4641611
## IC05_IC06 -1.33609529 0.1840463
## IC05_IC19 0.17290018 0.8630212
## IC07_IC13 -0.51253646 0.6092177
## IC08_IC11 1.03016331 0.3050052
## IC08_IC20 -0.36295554 0.7172766
## IC11_IC12 -0.43290137 0.6658635
## IC12_IC17 -1.53781893 0.1267256
## IC12_IC20 0.42146339 0.6741707
## IC13_IC14 1.05375076 0.2941142
## IC14_IC16 -0.21458017 0.8304589
## IC14_IC18 1.61329638 0.1093069
## IC14_IC20 -0.40703430 0.6847079
## IC15_IC17 0.59555346 0.5525951
## IC17_IC18 0.19429799 0.8462711
## IC18_IC19 -1.45202147 0.1491052
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC12 -0.23746199 -3.018572
## IC03_IC12 -0.10968540 47.802688
## IC03_IC13 0.14689897 -41.830474
## IC03_IC18 0.10599110 -39.024356
## IC04_IC06 0.06920072 171.330060
## IC04_IC11 -0.04688927 122.532051
## IC04_IC12 0.12141200 64.979549
## IC05_IC06 0.24171590 108.333373
## IC05_IC19 -0.03266458 144.266787
## IC07_IC13 0.10889826 -19.100543
## IC08_IC11 -0.18535529 86.526697
## IC08_IC20 0.04925293 -6.335306
## IC11_IC12 0.05396153 2.400852
## IC12_IC17 0.27864192 53.048196
## IC12_IC20 -0.06860982 -49.674870
## IC13_IC14 -0.18481380 -167.461254
## IC14_IC16 0.01263080 -130.472958
## IC14_IC18 -0.28359193 -121.742213
## IC14_IC20 0.07682061 -89.261416
## IC15_IC17 -0.10667706 33.886721
## IC17_IC18 -0.02405240 11.432187
## IC18_IC19 0.25652223 -11.191357
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC12 13.854534 -0.03816727
## IC03_IC12 64.675794 0.79428995
## IC03_IC13 -24.957368 -0.25384115
## IC03_IC18 -22.151250 -0.50040117
## IC04_IC06 188.203166 1.13495882
## IC04_IC11 139.405158 -0.76453816
## IC04_IC12 81.852655 1.66380603
## IC05_IC06 125.206479 -0.51261543
## IC05_IC19 161.139894 -0.57251121
## IC07_IC13 -2.227437 -0.09842180
## IC08_IC11 103.399803 1.13814737
## IC08_IC20 10.537800 1.51494716
## IC11_IC12 19.273958 -1.32796305
## IC12_IC17 69.921302 -0.72988394
## IC12_IC20 -32.801764 0.90860082
## IC13_IC14 -150.588148 -1.35806501
## IC14_IC16 -113.599852 0.13142358
## IC14_IC18 -104.869107 -0.20654360
## IC14_IC20 -72.388310 0.56155949
## IC15_IC17 50.759827 0.36209166
## IC17_IC18 28.305294 0.63832701
## IC18_IC19 5.681749 -0.42356551
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC12 0.96961520 0.007308656
## IC03_IC12 0.42853204 -0.137625535
## IC03_IC13 0.80003535 0.031516241
## IC03_IC18 0.61767251 0.097825153
## IC04_IC06 0.25856422 -0.180959924
## IC04_IC11 0.44598750 0.093129003
## IC04_IC12 0.09865626 -0.299153195
## IC05_IC06 0.60912500 0.109685062
## IC05_IC19 0.56800400 0.109273027
## IC07_IC13 0.92175502 0.017303175
## IC08_IC11 0.25723569 -0.183666063
## IC08_IC20 0.13231041 -0.228014478
## IC11_IC12 0.18661010 0.251668418
## IC12_IC17 0.46682674 0.112394078
## IC12_IC20 0.36530935 -0.101880519
## IC13_IC14 0.17688930 0.317164668
## IC14_IC16 0.89565153 0.005425268
## IC14_IC18 0.83670237 0.024557876
## IC14_IC20 0.57542163 -0.128090838
## IC15_IC17 0.71789496 -0.054704957
## IC17_IC18 0.52442859 -0.107790652
## IC18_IC19 0.67261064 0.072443272
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC12 -2.899699 14.212483
## IC03_IC12 62.464131 79.576312
## IC03_IC13 -75.998411 -58.886230
## IC03_IC18 -19.857369 -2.745187
## IC04_IC06 174.940114 192.052296
## IC04_IC11 123.810415 140.922597
## IC04_IC12 45.860327 62.972509
## IC05_IC06 108.418974 125.531156
## IC05_IC19 140.306037 157.418219
## IC07_IC13 -24.289551 -7.177369
## IC08_IC11 80.221042 97.333224
## IC08_IC20 -15.558103 1.554078
## IC11_IC12 4.063025 21.175207
## IC12_IC17 45.276067 62.388249
## IC12_IC20 -56.337451 -39.225269
## IC13_IC14 -151.799981 -134.687800
## IC14_IC16 -140.822000 -123.709819
## IC14_IC18 -142.507275 -125.395094
## IC14_IC20 -115.040319 -97.928137
## IC15_IC17 51.495849 68.608031
## IC17_IC18 44.578449 61.690631
## IC18_IC19 40.437618 57.549800
## SCequalRRB_vs_SCoverRRB.repBF SCcorr_Disc.r SCcorr_Disc.t
## IC01_IC12 0.4805766 0.021941964 0.42665054
## IC03_IC12 0.9548092 0.122208052 0.97184339
## IC03_IC13 0.6423274 -0.215653028 -2.15128320
## IC03_IC18 0.7911175 0.002136498 0.32147605
## IC04_IC06 0.7130299 -0.149270513 -1.72131113
## IC04_IC11 0.7868123 0.053684900 0.96235535
## IC04_IC12 0.6691546 -0.043082620 -0.32111128
## IC05_IC06 0.6753121 -0.027760400 -0.54631033
## IC05_IC19 0.7225772 0.089447081 1.03701535
## IC07_IC13 0.6771370 0.028251426 0.25964948
## IC08_IC11 1.3433948 0.116140332 1.60916326
## IC08_IC20 0.9225405 0.061020958 0.48328981
## IC11_IC12 1.3937420 -0.097223948 -0.95708854
## IC12_IC17 0.7776360 -0.098155586 -1.12468193
## IC12_IC20 1.0048041 0.079352256 0.45291808
## IC13_IC14 0.4170092 -0.059875454 -0.39990554
## IC14_IC16 0.6897642 -0.131419580 -1.33213089
## IC14_IC18 0.3127558 0.082481691 1.08778758
## IC14_IC20 0.6522730 0.109434809 0.72915968
## IC15_IC17 0.7417239 0.039269703 0.07988827
## IC17_IC18 0.8238009 0.127776852 1.07803010
## IC18_IC19 0.5884339 -0.012813809 -0.55935459
## SCcorr_Disc.pval SCcorr_Rep.r SCcorr_Rep.t SCcorr_Rep.pval
## IC01_IC12 0.67039832 0.003233085 0.15461485 0.87737433
## IC03_IC12 0.33308334 -0.033982102 -0.48475563 0.62869809
## IC03_IC13 0.03345810 -0.126963611 -1.12300938 0.26358581
## IC03_IC18 0.74840893 -0.153791633 -1.53703149 0.12681306
## IC04_IC06 0.08777141 0.161738712 1.67818048 0.09581035
## IC04_IC11 0.33780635 -0.018351087 -0.18924902 0.85020459
## IC04_IC12 0.74868464 0.008786831 0.44549366 0.65673363
## IC05_IC06 0.58586694 0.009072133 -0.13993955 0.88893298
## IC05_IC19 0.30181386 -0.036191267 -0.34401951 0.73140964
## IC07_IC13 0.79557911 0.056376570 0.26710893 0.78982547
## IC08_IC11 0.11020834 0.187959425 2.27255249 0.02476101
## IC08_IC20 0.62977061 0.200556463 2.05566773 0.04189683
## IC11_IC12 0.34044681 -0.124975082 -1.24159464 0.21671088
## IC12_IC17 0.26296847 -0.203831211 -2.31675788 0.02214415
## IC12_IC20 0.65142557 0.219000361 2.19566290 0.02996203
## IC13_IC14 0.68993704 -0.016340357 0.07893432 0.93721109
## IC14_IC16 0.18534161 0.065191885 0.98790761 0.32510632
## IC14_IC18 0.27886889 0.069255232 0.99406973 0.32210933
## IC14_IC20 0.46732477 -0.034469408 -0.67167717 0.50302870
## IC15_IC17 0.93645920 -0.068463813 -1.08495884 0.28002795
## IC17_IC18 0.28318260 -0.097275106 -1.20345834 0.23107323
## IC18_IC19 0.57696224 -0.028593224 -0.52451062 0.60085236
## SCcorr.repBF RRBcorr_Disc.r RRBcorr_Disc.t RRBcorr_Disc.pval
## IC01_IC12 0.6954284 0.022253885 0.41920559 0.67581534
## IC03_IC12 0.4613062 -0.052197602 -0.61712249 0.53832312
## IC03_IC13 0.9918510 -0.100984042 -0.98383037 0.32717839
## IC03_IC18 0.9684752 0.087762907 1.07295336 0.28544500
## IC04_IC06 0.1627004 -0.088447737 -1.13301352 0.25946725
## IC04_IC11 0.5096450 0.001223994 0.85033233 0.39683381
## IC04_IC12 0.6674887 -0.018878034 -0.10172859 0.91914190
## IC05_IC06 0.6776660 0.129883126 1.02441277 0.30770101
## IC05_IC19 0.4593306 0.111258537 1.19479533 0.23452312
## IC07_IC13 0.7256737 0.006504303 0.17706303 0.85975739
## IC08_IC11 8.0866009 0.140225485 1.70980028 0.08988736
## IC08_IC20 3.1215669 0.004305451 -0.25344479 0.80035822
## IC11_IC12 1.4854833 -0.065262479 -0.60958908 0.54328656
## IC12_IC17 7.0481993 0.268013929 2.12742673 0.03543136
## IC12_IC20 3.6324648 0.007883081 -0.64080908 0.52286861
## IC13_IC14 0.6630706 -0.033549747 -0.07222455 0.94254345
## IC14_IC16 0.2967348 -0.020705032 -0.12606605 0.89989062
## IC14_IC18 1.1442546 0.052195690 0.95395157 0.34202584
## IC14_IC20 0.5359185 0.059448943 0.13091575 0.89606118
## IC15_IC17 0.9042265 0.059630717 0.24894077 0.80383217
## IC17_IC18 0.3945256 0.066930613 0.21592829 0.82941025
## IC18_IC19 0.8030510 0.078735341 0.11598313 0.90785978
## RRBcorr_Rep.r RRBcorr_Rep.t RRBcorr_Rep.pval RRBcorr.repBF
## IC01_IC12 -0.031570729 0.43793056 0.662192433 0.7712664
## IC03_IC12 -0.268313828 -2.64576491 0.009197724 8.0245238
## IC03_IC13 -0.105545423 -0.97743952 0.330239493 1.1285575
## IC03_IC18 -0.191165725 -1.56211423 0.120789313 0.4190989
## IC04_IC06 0.131186457 1.17183634 0.243491459 0.3703527
## IC04_IC11 -0.006080479 0.69246450 0.489930085 0.8848918
## IC04_IC12 -0.132022831 -1.28323728 0.201782973 1.1339116
## IC05_IC06 0.008273737 0.01175331 0.990641170 0.5392303
## IC05_IC19 0.020045801 0.12026905 0.904463122 0.5252455
## IC07_IC13 0.080865498 0.37982124 0.704722665 0.7459577
## IC08_IC11 0.234578549 2.30655588 0.022725465 8.9081085
## IC08_IC20 0.135383657 1.14996703 0.252352808 0.8330652
## IC11_IC12 -0.029488716 -0.56718002 0.571609043 0.8222671
## IC12_IC17 -0.111746204 -0.72156532 0.471909306 0.1178531
## IC12_IC20 0.291944708 2.56164090 0.011605625 1.4242822
## IC13_IC14 0.240763309 2.83555055 0.005337477 4.5492368
## IC14_IC16 0.131510531 1.53218304 0.128004284 1.1475421
## IC14_IC18 0.206087538 2.46637852 0.015003756 8.0998250
## IC14_IC20 -0.196980403 -2.08910559 0.038726525 1.8179576
## IC15_IC17 0.181375760 1.51968554 0.131115427 1.4923754
## IC17_IC18 -0.131431759 -1.21259283 0.227572306 0.8816555
## IC18_IC19 -0.085669113 -1.45846024 0.147221837 1.1002032
## SumSCRRB_Disc.r SumSCRRB_Disc.t SumSCRRB_Disc.pval SumSCRRB_Rep.r
## IC01_IC12 0.02901956 0.5310098 0.59639342 -0.01216495
## IC03_IC12 0.05598002 0.2238562 0.82324983 -0.14992126
## IC03_IC13 -0.21386179 -1.9781943 0.05019802 -0.14306998
## IC03_IC18 0.05422217 0.8827275 0.37914842 -0.20242756
## IC04_IC06 -0.15934662 -1.8436439 0.06770220 0.18064959
## IC04_IC11 0.03906505 1.0959706 0.27528630 -0.01646374
## IC04_IC12 -0.04208466 -0.2700538 0.78758276 -0.05450550
## IC05_IC06 0.05825782 0.2156622 0.82961723 0.01057013
## IC05_IC19 0.13048683 1.3482337 0.18012260 -0.01768556
## IC07_IC13 0.02407416 0.2742754 0.78434455 0.07926642
## IC08_IC11 0.16676443 2.0117014 0.04649155 0.24768499
## IC08_IC20 0.04615055 0.1748611 0.86148346 0.21130671
## IC11_IC12 -0.10848355 -0.9888179 0.32474187 -0.10658917
## IC12_IC17 0.09204190 0.4721155 0.63770172 -0.20289733
## IC12_IC20 0.06138142 -0.0445753 0.96451992 0.29703865
## IC13_IC14 -0.06286582 -0.3133240 0.75457845 0.09966974
## IC14_IC16 -0.10620909 -0.8854186 0.37770170 0.10922603
## IC14_IC18 0.09015854 1.3279541 0.18671367 0.14686474
## IC14_IC20 0.11370131 0.5391124 0.59080811 -0.11684976
## IC15_IC17 0.06380923 0.2176714 0.82805483 0.03307955
## IC17_IC18 0.13122882 0.8776867 0.38186762 -0.13299202
## IC18_IC19 0.03812920 -0.3085780 0.75817758 -0.06083131
## SumSCRRB_Rep.t SumSCRRB_Rep.pval SumSCRRB.repBF zds_Disc.r
## IC01_IC12 0.32693046 0.744267295 0.7303170 0.002657793
## IC03_IC12 -1.61197695 0.109489038 1.1153558 0.136402752
## IC03_IC13 -1.27302692 0.205371003 1.3712965 -0.107756293
## IC03_IC18 -1.82520174 0.070358059 0.5953742 -0.058854280
## IC04_IC06 1.76067758 0.080738597 0.1326764 -0.061618574
## IC04_IC11 0.09844675 0.921735250 0.5455604 0.043233647
## IC04_IC12 -0.35513921 0.723083828 0.7448749 -0.022345781
## IC05_IC06 -0.09186411 0.926953069 0.6863482 -0.112569212
## IC05_IC19 -0.18477365 0.853706029 0.3926020 -0.003416208
## IC07_IC13 0.36927339 0.712548753 0.7484291 0.018705713
## IC08_IC11 2.70863823 0.007703423 22.7219001 -0.001563469
## IC08_IC20 2.02789026 0.044697063 2.3252346 0.047133160
## IC11_IC12 -1.12832858 0.261342137 1.3193817 -0.034806562
## IC12_IC17 -1.93457578 0.055301730 1.0775135 -0.266888039
## IC12_IC20 2.85725380 0.005006840 4.8818904 0.059725308
## IC13_IC14 1.46765418 0.144709887 0.9384866 -0.026012431
## IC14_IC16 1.43006656 0.155192719 0.5141249 -0.093700405
## IC14_IC18 1.94080748 0.054532693 4.1581081 0.031713651
## IC14_IC20 -1.47966288 0.141479102 0.7598084 0.048866197
## IC15_IC17 -0.03661856 0.970847557 0.6895181 -0.008920903
## IC17_IC18 -1.41513365 0.159515783 0.5153622 0.058791490
## IC18_IC19 -1.12958502 0.260814123 1.1256414 -0.064881831
## zds_Disc.t zds_Disc.pval zds_Rep.r zds_Rep.t zds_Rep.pval
## IC01_IC12 0.02685358 0.978621128 0.023309478 -0.158594291 0.87424447
## IC03_IC12 1.43101724 0.155024347 0.136734419 1.453557345 0.14857512
## IC03_IC13 -0.99853453 0.320029498 -0.062880426 -0.430408517 0.66763968
## IC03_IC18 -0.56954469 0.570051184 -0.035681414 -0.437707139 0.66235397
## IC04_IC06 -0.59680280 0.551763355 0.082200387 0.912711949 0.36315105
## IC04_IC11 0.15616824 0.876162830 -0.014899531 -0.587190628 0.55813481
## IC04_IC12 -0.19469984 0.845957187 0.092781160 1.438981648 0.15265506
## IC05_IC06 -1.30640519 0.193913203 0.004027948 -0.149721114 0.88122595
## IC05_IC19 -0.11501962 0.908621815 -0.049681652 -0.445095420 0.65702061
## IC07_IC13 0.08289200 0.934075525 0.006338976 -0.005453223 0.99565767
## IC08_IC11 -0.04063034 0.967658120 0.042788914 0.643662229 0.52097367
## IC08_IC20 0.60851801 0.543994106 0.119371439 1.294230664 0.19797173
## IC11_IC12 -0.33824990 0.735765943 -0.109081175 -0.878497723 0.38135921
## IC12_IC17 -2.80788842 0.005822396 -0.137767387 -1.880084783 0.06242402
## IC12_IC20 0.79435046 0.428559577 0.037417649 0.646003575 0.51946125
## IC13_IC14 -0.28190050 0.778505391 -0.170165849 -1.763193184 0.08031152
## IC14_IC16 -1.08983062 0.277971435 -0.016876321 -0.110565618 0.91213814
## IC14_IC18 0.22174035 0.824892929 -0.060486527 -0.624828062 0.53322281
## IC14_IC20 0.54296251 0.588162711 0.090168120 0.851331964 0.39621295
## IC15_IC17 -0.11415196 0.909308101 -0.185111864 -2.076675164 0.03988013
## IC17_IC18 0.73741185 0.462311380 -0.015996310 -0.353999369 0.72393577
## IC18_IC19 -0.55720363 0.578426142 0.025111679 0.423527791 0.67263808
## zds.repBF SumSCRRB_SCequalRRB_Disc.r SumSCRRB_SCequalRRB_Disc.t
## IC01_IC12 0.7031229 0.08453430 0.582817696
## IC03_IC12 2.0059578 -0.01155316 -0.077144165
## IC03_IC13 0.7063224 -0.22792682 -1.632784157
## IC03_IC18 0.7661179 -0.14515754 -0.756493858
## IC04_IC06 0.6019329 -0.23983031 -2.208760572
## IC04_IC11 0.7263378 0.07979150 1.019421240
## IC04_IC12 1.0215623 0.00970710 -0.067932710
## IC05_IC06 0.5024849 0.08476974 0.160525539
## IC05_IC19 0.7523901 0.26829860 1.747233429
## IC07_IC13 0.6984898 -0.06478857 -0.314035300
## IC08_IC11 0.7679910 0.20314909 2.070569334
## IC08_IC20 1.4464655 0.10572511 0.517053629
## IC11_IC12 0.9605286 -0.16341962 -1.173901714
## IC12_IC17 3.1878337 0.09933078 0.779981721
## IC12_IC20 0.8578478 0.17634246 0.786818610
## IC13_IC14 1.9240908 -0.21411146 -1.145432836
## IC14_IC16 0.5499091 -0.08390345 -1.081104852
## IC14_IC18 0.7130210 0.08960937 1.124692464
## IC14_IC20 0.9848591 0.08161356 0.223687292
## IC15_IC17 2.3174567 0.04856125 0.211003814
## IC17_IC18 0.5522379 0.09205547 -0.008428301
## IC18_IC19 0.6018958 0.09847882 0.086778309
## SumSCRRB_SCequalRRB_Disc.pval SumSCRRB_SCequalRRB_Rep.r
## IC01_IC12 0.56188888 -0.051429808
## IC03_IC12 0.93872894 -0.252101805
## IC03_IC13 0.10700501 -0.070945668
## IC03_IC18 0.45189173 -0.346254372
## IC04_IC06 0.03046698 0.192596559
## IC04_IC11 0.31151345 -0.068780809
## IC04_IC12 0.94603287 -0.216566499
## IC05_IC06 0.87292946 -0.044465200
## IC05_IC19 0.08498255 -0.002427627
## IC07_IC13 0.75442822 0.133901003
## IC08_IC11 0.04208890 0.237543592
## IC08_IC20 0.60674866 0.226051761
## IC11_IC12 0.24441293 -0.217659882
## IC12_IC17 0.43803105 -0.232918057
## IC12_IC20 0.43404386 0.385294634
## IC13_IC14 0.25593092 0.330555609
## IC14_IC16 0.28336103 0.159239090
## IC14_IC18 0.26456122 0.244718725
## IC14_IC20 0.82365218 -0.220639840
## IC15_IC17 0.83349736 0.149623778
## IC17_IC18 0.99329924 -0.137009332
## IC18_IC19 0.93109548 -0.128455963
## SumSCRRB_SCequalRRB_Rep.t SumSCRRB_SCequalRRB_Rep.pval
## IC01_IC12 -0.03152289 0.974935179
## IC03_IC12 -2.13849712 0.035689420
## IC03_IC13 -0.57029448 0.570160190
## IC03_IC18 -2.85673153 0.005517810
## IC04_IC06 1.39921881 0.165815584
## IC04_IC11 -0.20727979 0.836345746
## IC04_IC12 -1.61250712 0.110996817
## IC05_IC06 -0.36784294 0.714012828
## IC05_IC19 -0.04605968 0.963383500
## IC07_IC13 1.12000533 0.266238495
## IC08_IC11 1.82595541 0.071784762
## IC08_IC20 1.60816070 0.111946658
## IC11_IC12 -1.62430129 0.108452055
## IC12_IC17 -1.49044391 0.140246358
## IC12_IC20 2.97929133 0.003877243
## IC13_IC14 2.92181083 0.004580470
## IC14_IC16 1.21745530 0.227197717
## IC14_IC18 2.56785351 0.012195743
## IC14_IC20 -1.71566351 0.090297943
## IC15_IC17 0.99306528 0.323829438
## IC17_IC18 -0.83574009 0.405920958
## IC18_IC19 -1.06094407 0.292075276
## SumSCRRB_SCequalRRB.repBF zds_SCequalRRB_Disc.r zds_SCequalRRB_Disc.t
## IC01_IC12 0.63757610 -0.304764964 -2.62297571
## IC03_IC12 2.38392316 0.053429273 0.42918135
## IC03_IC13 0.61429155 0.056651156 0.62283881
## IC03_IC18 13.08618251 -0.101232305 -0.74846253
## IC04_IC06 0.07384865 -0.108473930 -0.64194979
## IC04_IC11 0.48880187 -0.082045454 -1.10763632
## IC04_IC12 1.42618135 0.043130364 0.48226160
## IC05_IC06 0.69969425 -0.062024451 -0.31237974
## IC05_IC19 0.30896018 0.059003703 0.35474036
## IC07_IC13 0.78795137 -0.136142503 -1.27015592
## IC08_IC11 3.58418088 -0.034850679 -0.78564575
## IC08_IC20 1.90443486 0.181212224 2.07937145
## IC11_IC12 2.48406719 0.084597673 0.62105121
## IC12_IC17 0.59018732 -0.150194639 -1.03967465
## IC12_IC20 16.66623696 -0.042696936 -0.03270187
## IC13_IC14 0.79291236 -0.315130289 -2.94806341
## IC14_IC16 0.39291048 -0.158107047 -1.03140685
## IC14_IC18 10.85032508 -0.255930003 -2.34168223
## IC14_IC20 1.20057044 0.080616740 0.72507206
## IC15_IC17 0.98902056 -0.019421667 -0.24120391
## IC17_IC18 0.83935203 0.065140888 1.13799473
## IC18_IC19 0.88977031 -0.000418521 0.34291476
## zds_SCequalRRB_Disc.pval zds_SCequalRRB_Rep.r zds_SCequalRRB_Rep.t
## IC01_IC12 0.010689632 0.007186694 -0.15359940
## IC03_IC12 0.669109307 0.238823062 2.03973254
## IC03_IC13 0.535414365 -0.048901495 -0.28393741
## IC03_IC18 0.456688706 -0.034137618 -0.29397109
## IC04_IC06 0.523002532 0.009091615 0.09055235
## IC04_IC11 0.271810462 0.029230550 0.03236764
## IC04_IC12 0.631124715 0.019854650 0.33948477
## IC05_IC06 0.755680619 0.046155169 0.29663956
## IC05_IC19 0.723850643 0.024552826 0.26943251
## IC07_IC13 0.208234888 0.059794448 0.35273846
## IC08_IC11 0.434726334 0.045497331 0.59301281
## IC08_IC20 0.041248059 -0.043494587 -0.34197000
## IC11_IC12 0.536583057 0.061218318 0.74893694
## IC12_IC17 0.302068917 -0.235150588 -2.21792356
## IC12_IC20 0.974005398 -0.139094881 -0.93198121
## IC13_IC14 0.004342648 0.004083003 0.02722981
## IC14_IC16 0.305900575 0.002437298 0.01237293
## IC14_IC18 0.022048419 -0.032115490 -0.36308560
## IC14_IC20 0.470824746 0.106186082 0.80047945
## IC15_IC17 0.810102206 -0.321794127 -2.98895302
## IC17_IC18 0.259002731 -0.150301757 -1.51190713
## IC18_IC19 0.732690251 0.136700449 1.26720853
## zds_SCequalRRB_Rep.pval zds_SCequalRRB.repBF zds_SCoverRRB_Disc.r
## IC01_IC12 0.878332731 0.15178244 0.12514764
## IC03_IC12 0.044852452 2.92923690 0.31483942
## IC03_IC13 0.777230467 0.59291183 -0.35132901
## IC03_IC18 0.769581356 0.69309880 0.15002844
## IC04_IC06 0.928086526 0.61291571 0.16360124
## IC04_IC11 0.974263725 0.50296076 0.18858525
## IC04_IC12 0.735180922 0.73826492 -0.09421679
## IC05_IC06 0.767550870 0.66639479 -0.11190641
## IC05_IC19 0.788327041 0.72427852 -0.22911017
## IC07_IC13 0.725260931 0.38410749 0.49912811
## IC08_IC11 0.554933175 0.51876484 -0.27380487
## IC08_IC20 0.733317305 0.16871633 0.07577193
## IC11_IC12 0.456206960 0.92429365 -0.15366924
## IC12_IC17 0.029547179 5.72240258 -0.48501988
## IC12_IC20 0.354297525 0.88759179 0.27712226
## IC13_IC14 0.978347811 0.07565999 0.01863420
## IC14_IC16 0.990160505 0.52988358 -0.32092795
## IC14_IC18 0.717548856 0.27604698 -0.07593056
## IC14_IC20 0.425928307 0.96437838 0.22447372
## IC15_IC17 0.003769356 8.76381163 -0.22142959
## IC17_IC18 0.134704708 0.38382072 0.17504276
## IC18_IC19 0.208950037 1.27274777 0.16724776
## zds_SCoverRRB_Disc.t zds_SCoverRRB_Disc.pval zds_SCoverRRB_Rep.r
## IC01_IC12 0.7490161 0.457925466 -0.01383797
## IC03_IC12 2.1403273 0.038041964 -0.09835797
## IC03_IC13 -2.1436523 0.037758465 -0.05521216
## IC03_IC18 0.8174150 0.418197561 0.07306033
## IC04_IC06 0.7846089 0.436984271 -0.02879048
## IC04_IC11 1.5147114 0.137162322 0.03255199
## IC04_IC12 -0.1475780 0.883365684 -0.02818725
## IC05_IC06 -0.6188697 0.539267142 0.05835806
## IC05_IC19 -1.1872426 0.241649389 -0.04303200
## IC07_IC13 3.3828977 0.001538571 -0.04158595
## IC08_IC11 -1.6807905 0.100054802 -0.22139803
## IC08_IC20 0.1820616 0.856389971 0.10609126
## IC11_IC12 -0.6716776 0.505381795 -0.06426293
## IC12_IC17 -3.2219896 0.002428651 -0.07174130
## IC12_IC20 1.4104955 0.165586954 0.17403741
## IC13_IC14 -0.1326373 0.895098975 -0.26895060
## IC14_IC16 -1.9008935 0.064032091 -0.08336991
## IC14_IC18 -0.3440332 0.732498079 -0.16472460
## IC14_IC20 1.4590414 0.151820584 -0.06365687
## IC15_IC17 -1.8183807 0.075975895 -0.44367638
## IC17_IC18 1.0272717 0.310036782 -0.08765311
## IC18_IC19 0.9430599 0.350918942 0.04113726
## zds_SCoverRRB_Rep.t zds_SCoverRRB_Rep.pval zds_SCoverRRB.repBF
## IC01_IC12 -0.1476998 0.883286514 0.57697056
## IC03_IC12 -0.6601649 0.512752442 0.12125287
## IC03_IC13 -0.4324940 0.667595070 0.36356118
## IC03_IC18 0.4123109 0.682208585 0.73059618
## IC04_IC06 -0.1428225 0.887113396 0.56890841
## IC04_IC11 0.1844043 0.854583787 0.45261707
## IC04_IC12 -0.1633849 0.870999053 0.70866468
## IC05_IC06 0.4374403 0.664033284 0.58228054
## IC05_IC19 -0.2953069 0.769214412 0.59585482
## IC07_IC13 -0.1405522 0.888895622 0.03157664
## IC08_IC11 -1.5481815 0.129081609 2.28966778
## IC08_IC20 0.6816124 0.499225579 0.83225718
## IC11_IC12 -0.3733463 0.710768595 0.73294943
## IC12_IC17 -0.5284882 0.599942240 0.12966061
## IC12_IC20 1.1605112 0.252394877 1.34700523
## IC13_IC14 -1.7524818 0.086989107 1.69900539
## IC14_IC16 -0.5476998 0.586797138 0.50748612
## IC14_IC18 -1.0329461 0.307540628 1.06590557
## IC14_IC20 -0.3057028 0.761341021 0.33330483
## IC15_IC17 -3.0464006 0.003992099 44.91523696
## IC17_IC18 -0.5159395 0.608602301 0.43959511
## IC18_IC19 0.2933284 0.770715661 0.65484908
## VinelandABC_Disc.r VinelandABC_Disc.t VinelandABC_Disc.pval
## IC01_IC12 0.117928538 1.28839305 0.20008779
## IC03_IC12 -0.072641630 -0.90259428 0.36854945
## IC03_IC13 -0.134772608 -1.21171075 0.22800372
## IC03_IC18 -0.061046059 -0.40567733 0.68570212
## IC04_IC06 0.071190982 0.74733356 0.45632415
## IC04_IC11 -0.103254277 -1.19071242 0.23611656
## IC04_IC12 0.002441248 0.09422101 0.92509074
## IC05_IC06 0.057668657 0.34139602 0.73340254
## IC05_IC19 -0.143747879 -1.51313280 0.13287524
## IC07_IC13 0.073694498 0.64281122 0.52157295
## IC08_IC11 -0.041406876 -0.28936272 0.77280316
## IC08_IC20 -0.012128605 -0.05912888 0.95294781
## IC11_IC12 0.056106901 0.59532805 0.55274527
## IC12_IC17 0.016795507 0.21690377 0.82865168
## IC12_IC20 0.027880754 0.22720128 0.82065386
## IC13_IC14 0.025951362 0.40944604 0.68294229
## IC14_IC16 0.202370386 2.00841180 0.04684481
## IC14_IC18 0.118454122 1.22434700 0.22321958
## IC14_IC20 -0.182685897 -2.06849525 0.04074162
## IC15_IC17 -0.025714298 -0.44972704 0.65371858
## IC17_IC18 -0.162183284 -1.71739838 0.08848605
## IC18_IC19 -0.095770335 -1.10850604 0.26986009
## VinelandABC_Rep.r VinelandABC_Rep.t VinelandABC_Rep.pval
## IC01_IC12 0.177064715 1.84236559 0.067791395
## IC03_IC12 0.007354335 0.03012218 0.976017651
## IC03_IC13 0.075485086 0.99794471 0.320234088
## IC03_IC18 -0.002086721 -0.06239543 0.950347592
## IC04_IC06 -0.250785296 -2.79756054 0.005964628
## IC04_IC11 -0.112075279 -1.25531477 0.211706170
## IC04_IC12 -0.131798765 -1.22488546 0.222921592
## IC05_IC06 0.039613563 0.31034038 0.756818969
## IC05_IC19 -0.211936396 -2.24557972 0.026487633
## IC07_IC13 -0.046360656 -0.64138661 0.522445833
## IC08_IC11 0.007092690 0.30054629 0.764259781
## IC08_IC20 -0.214624658 -2.41438023 0.017210910
## IC11_IC12 0.056210538 0.70922509 0.479505554
## IC12_IC17 0.053290790 0.37911699 0.705244219
## IC12_IC20 -0.077436310 -0.80379023 0.423044058
## IC13_IC14 0.026756794 0.34334322 0.731917061
## IC14_IC16 -0.075385606 -0.66711643 0.505927395
## IC14_IC18 0.062231634 0.77226639 0.441414333
## IC14_IC20 -0.218013422 -2.55029050 0.011970586
## IC15_IC17 -0.097459593 -1.20257392 0.231414244
## IC17_IC18 -0.065270821 -0.87488330 0.383315323
## IC18_IC19 0.141866569 1.51854409 0.131402514
## VinelandABC.repBF VinelandABC_SCequalRRB_Disc.r
## IC01_IC12 3.5207973 0.1067004255
## IC03_IC12 0.5607317 -0.1006967711
## IC03_IC13 0.3406041 -0.2073287286
## IC03_IC18 0.6802684 0.0144610042
## IC04_IC06 1.4756638 -0.0568329390
## IC04_IC11 1.5367022 -0.1012964197
## IC04_IC12 0.9670556 -0.1555691202
## IC05_IC06 0.7353533 0.1376289705
## IC05_IC19 7.4441019 -0.2372982915
## IC07_IC13 0.5695473 0.1002723097
## IC08_IC11 0.6711232 -0.3478406641
## IC08_IC20 3.2002400 -0.1796873079
## IC11_IC12 0.8989774 -0.1793628342
## IC12_IC17 0.7492798 -0.1076469471
## IC12_IC20 0.7447499 -0.0006189235
## IC13_IC14 0.7423939 0.0191741885
## IC14_IC16 0.1446152 0.2976287607
## IC14_IC18 0.8937073 0.1247968842
## IC14_IC20 16.1666432 -0.2929031944
## IC15_IC17 1.2606998 0.0579360643
## IC17_IC18 0.8498884 -0.0794348038
## IC18_IC19 0.3986396 -0.1249865158
## VinelandABC_SCequalRRB_Disc.t VinelandABC_SCequalRRB_Disc.pval
## IC01_IC12 0.7193601 0.474313869
## IC03_IC12 -0.7420718 0.460526571
## IC03_IC13 -1.3714328 0.174620368
## IC03_IC18 0.4702458 0.639641254
## IC04_IC06 -0.4998388 0.618756230
## IC04_IC11 -0.8604551 0.392475830
## IC04_IC12 -1.0730762 0.286922336
## IC05_IC06 0.7972287 0.428014151
## IC05_IC19 -1.9426155 0.056085994
## IC07_IC13 0.5657419 0.573378368
## IC08_IC11 -2.7921213 0.006745789
## IC08_IC20 -1.2031270 0.232980570
## IC11_IC12 -1.3611376 0.177836017
## IC12_IC17 -0.5094735 0.612022813
## IC12_IC20 -0.1981194 0.843525917
## IC13_IC14 0.2725603 0.785993679
## IC14_IC16 2.2322891 0.028797530
## IC14_IC18 0.9581084 0.341305658
## IC14_IC20 -2.3405824 0.022108601
## IC15_IC17 0.1664686 0.868268122
## IC17_IC18 -0.5888137 0.557881568
## IC18_IC19 -1.1508268 0.253719517
## VinelandABC_SCequalRRB_Rep.r VinelandABC_SCequalRRB_Rep.t
## IC01_IC12 -0.028225551 -0.3172077
## IC03_IC12 -0.062394517 -0.6723051
## IC03_IC13 0.028136233 0.4248429
## IC03_IC18 0.081908225 0.5913030
## IC04_IC06 -0.378369363 -3.3696812
## IC04_IC11 0.203348437 1.5505413
## IC04_IC12 -0.129102367 -0.7942286
## IC05_IC06 0.196632228 1.4833985
## IC05_IC19 -0.343753200 -2.9453116
## IC07_IC13 -0.129714056 -1.1986963
## IC08_IC11 0.109095820 1.1808637
## IC08_IC20 -0.380912406 -3.4161414
## IC11_IC12 0.053705671 0.7809750
## IC12_IC17 0.100371752 0.7427915
## IC12_IC20 -0.003099587 0.0138864
## IC13_IC14 0.043940453 0.5954126
## IC14_IC16 -0.045257523 -0.3420221
## IC14_IC18 0.008059356 0.2112618
## IC14_IC20 -0.294362907 -2.6411860
## IC15_IC17 -0.106195933 -1.2274268
## IC17_IC18 -0.093917095 -0.9365967
## IC18_IC19 0.157557469 1.3185571
## VinelandABC_SCequalRRB_Rep.pval VinelandABC_SCequalRRB.repBF
## IC01_IC12 0.751955781 0.56125646
## IC03_IC12 0.503428338 0.87585412
## IC03_IC13 0.672151604 0.33929349
## IC03_IC18 0.556072065 0.83252710
## IC04_IC06 0.001184757 23.41869444
## IC04_IC11 0.125166136 0.54954983
## IC04_IC12 0.429535418 0.93927505
## IC05_IC06 0.142103918 1.87510553
## IC05_IC19 0.004279808 38.01376662
## IC07_IC13 0.234370431 0.66420106
## IC08_IC11 0.241339263 0.02836349
## IC08_IC20 0.001022701 61.02756904
## IC11_IC12 0.437243122 0.30097777
## IC12_IC17 0.459897734 0.62415799
## IC12_IC20 0.988957004 0.69163219
## IC13_IC14 0.553336609 0.81526491
## IC14_IC16 0.733278256 0.14004165
## IC14_IC18 0.833248475 0.61992832
## IC14_IC20 0.010024113 20.86007442
## IC15_IC17 0.223450591 0.92236744
## IC17_IC18 0.351932765 1.05624652
## IC18_IC19 0.191276991 0.36574951
## VinelandABC_SCoverRRB_Disc.r VinelandABC_SCoverRRB_Disc.t
## IC01_IC12 0.17450773 1.1500832
## IC03_IC12 -0.05829850 -0.5443349
## IC03_IC13 -0.05493026 -0.2813511
## IC03_IC18 -0.13645941 -0.7687423
## IC04_IC06 0.23460265 1.5294011
## IC04_IC11 -0.10925131 -0.8061005
## IC04_IC12 0.15073623 0.8948775
## IC05_IC06 -0.01252665 -0.1058172
## IC05_IC19 -0.05625207 -0.3141004
## IC07_IC13 0.06692883 0.2902638
## IC08_IC11 0.24185171 1.7131367
## IC08_IC20 0.12353340 0.7455110
## IC11_IC12 0.30720503 2.0424524
## IC12_IC17 0.14696804 0.9127734
## IC12_IC20 0.06309952 0.4465113
## IC13_IC14 0.01091215 0.2148629
## IC14_IC16 0.11380876 0.8067673
## IC14_IC18 0.07760485 0.5524788
## IC14_IC20 -0.06185320 -0.5298528
## IC15_IC17 -0.11811210 -0.6428318
## IC17_IC18 -0.27698340 -1.9000671
## IC18_IC19 -0.04723721 -0.1989041
## VinelandABC_SCoverRRB_Disc.pval VinelandABC_SCoverRRB_Rep.r
## IC01_IC12 0.25646580 0.536418218
## IC03_IC12 0.58902280 0.165714100
## IC03_IC13 0.77979061 0.076907406
## IC03_IC18 0.44624818 -0.176766966
## IC04_IC06 0.13348894 -0.026735180
## IC04_IC11 0.42462069 -0.488919436
## IC04_IC12 0.37583455 -0.163987447
## IC05_IC06 0.91621952 -0.131175026
## IC05_IC19 0.75496364 -0.023597413
## IC07_IC13 0.77300968 0.055654422
## IC08_IC11 0.09388985 -0.158762892
## IC08_IC20 0.46001880 0.067336298
## IC11_IC12 0.04726877 0.005041906
## IC12_IC17 0.36645122 -0.096416357
## IC12_IC20 0.65746863 -0.193767438
## IC13_IC14 0.83088998 -0.012687804
## IC14_IC16 0.42424047 -0.107986615
## IC14_IC18 0.58348099 0.260483377
## IC14_IC20 0.59893938 -0.046077450
## IC15_IC17 0.52374645 -0.077664969
## IC17_IC18 0.06414327 -0.042239434
## IC18_IC19 0.84327532 0.126906127
## VinelandABC_SCoverRRB_Rep.t VinelandABC_SCoverRRB_Rep.pval
## IC01_IC12 3.86729079 0.0003763654
## IC03_IC12 1.08004316 0.2862861949
## IC03_IC13 0.62371849 0.5361854701
## IC03_IC18 -1.02013170 0.3135064402
## IC04_IC06 -0.24538734 0.8073516501
## IC04_IC11 -3.57126741 0.0009068565
## IC04_IC12 -1.02811846 0.3097789492
## IC05_IC06 -0.85571450 0.3970113293
## IC05_IC19 0.01214623 0.9903664593
## IC07_IC13 0.28468705 0.7772829569
## IC08_IC11 -0.93550241 0.3548774338
## IC08_IC20 0.32228361 0.7488366371
## IC11_IC12 0.11551878 0.9085844864
## IC12_IC17 -0.49286836 0.6246726416
## IC12_IC20 -1.40224746 0.1681895928
## IC13_IC14 -0.08894350 0.9295495843
## IC14_IC16 -0.62831023 0.5332027590
## IC14_IC18 1.71612132 0.0935084324
## IC14_IC20 -0.34506671 0.7317663965
## IC15_IC17 -0.48841989 0.6277929619
## IC17_IC18 -0.41620867 0.6793765600
## IC18_IC19 0.81873578 0.4175585985
## VinelandABC_SCoverRRB.repBF
## IC01_IC12 152.6207509
## IC03_IC12 0.6511909
## IC03_IC13 0.6945975
## IC03_IC18 1.1612551
## IC04_IC06 0.3246640
## IC04_IC11 51.7317298
## IC04_IC12 0.4726053
## IC05_IC06 0.8804839
## IC05_IC19 0.6811388
## IC07_IC13 0.7290715
## IC08_IC11 0.1869120
## IC08_IC20 0.7040206
## IC11_IC12 0.2743429
## IC12_IC17 0.4808452
## IC12_IC20 0.8014064
## IC13_IC14 0.6868982
## IC14_IC16 0.5094492
## IC14_IC18 2.1857454
## IC14_IC20 0.7359131
## IC15_IC17 0.7841706
## IC17_IC18 0.4343160
## IC18_IC19 0.7588176
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask5 = aovres$SCcorr.repBF>=10
mask6 = aovres$RRBcorr.repBF>=10
mask7 = aovres$SumSCRRB.repBF>=10
mask8 = aovres$zds.repBF>=10
mask9 = aovres$zds_SCequalRRB.repBF>=10
mask10 = aovres$zds_SCoverRRB.repBF>=10
mask11 = aovres$SumSCRRB_SCequalRRB.repBF>=10
mask12 = aovres$VinelandABC.repBF>=10
mask13 = aovres$VinelandABC_SCequalRRB.repBF>=10
mask14 = aovres$VinelandABC_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 | mask5 | mask6 | mask7 | mask8 | mask9 | mask10 | mask11 | mask12 | mask13 | mask14
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF",
"SCcorr_Disc.r","SCcorr_Rep.r","SCcorr_Disc.pval","SCcorr_Rep.pval","SCcorr.repBF",
"RRBcorr_Disc.r","RRBcorr_Rep.r","RRBcorr_Disc.pval","RRBcorr_Rep.pval","RRBcorr.repBF",
"SumSCRRB_Disc.r","SumSCRRB_Rep.r","SumSCRRB_Disc.pval","SumSCRRB_Rep.pval","SumSCRRB.repBF",
"zds_Disc.r","zds_Rep.r","zds_Disc.pval","zds_Rep.pval","zds.repBF",
"SumSCRRB_SCequalRRB_Disc.r","SumSCRRB_SCequalRRB_Rep.r",
"SumSCRRB_SCequalRRB_Disc.pval","SumSCRRB_SCequalRRB_Rep.pval","SumSCRRB_SCequalRRB.repBF",
"zds_SCequalRRB_Disc.r","zds_SCequalRRB_Rep.r",
"zds_SCequalRRB_Disc.pval","zds_SCequalRRB_Rep.pval","zds_SCequalRRB.repBF",
"zds_SCoverRRB_Disc.r","zds_SCoverRRB_Rep.r",
"zds_SCoverRRB_Disc.pval","zds_SCoverRRB_Rep.pval","zds_SCoverRRB.repBF",
"VinelandABC_Disc.r","VinelandABC_Rep.r",
"VinelandABC_Disc.pval","VinelandABC_Rep.pval","VinelandABC.repBF",
"VinelandABC_SCequalRRB_Disc.r","VinelandABC_SCequalRRB_Rep.r",
"VinelandABC_SCequalRRB_Disc.pval","VinelandABC_SCequalRRB_Rep.pval",
"VinelandABC_SCequalRRB.repBF",
"VinelandABC_SCoverRRB_Disc.r","VinelandABC_SCoverRRB_Rep.r",
"VinelandABC_SCoverRRB_Disc.pval","VinelandABC_SCoverRRB_Rep.pval",
"VinelandABC_SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF SCcorr_Disc.r SCcorr_Rep.r
## IC01_IC12 IC01_IC12 0.7636743 0.4923188 0.021941964 0.003233085
## IC03_IC12 IC03_IC12 16.6928467 1.7845001 0.122208052 -0.033982102
## IC03_IC13 IC03_IC13 11.6387970 9.6795040 -0.215653028 -0.126963611
## IC03_IC18 IC03_IC18 0.6298486 1.1861277 0.002136498 -0.153791633
## IC04_IC06 IC04_IC06 0.6636211 0.5358113 -0.149270513 0.161738712
## IC04_IC11 IC04_IC11 0.7585735 0.7054349 0.053684900 -0.018351087
## IC04_IC12 IC04_IC12 16.2382806 0.3722300 -0.043082620 0.008786831
## IC05_IC06 IC05_IC06 81.9293437 1.4657777 -0.027760400 0.009072133
## IC05_IC19 IC05_IC19 0.9141969 1.5631620 0.089447081 -0.036191267
## IC07_IC13 IC07_IC13 390.8687559 19.4748090 0.028251426 0.056376570
## IC08_IC11 IC08_IC11 0.7797743 0.7331277 0.116140332 0.187959425
## IC08_IC20 IC08_IC20 1.3733856 0.7193256 0.061020958 0.200556463
## IC11_IC12 IC11_IC12 0.7316592 10.3433042 -0.097223948 -0.124975082
## IC12_IC17 IC12_IC17 3.2894374 23.7664845 -0.098155586 -0.203831211
## IC12_IC20 IC12_IC20 0.7625460 1.2533726 0.079352256 0.219000361
## IC13_IC14 IC13_IC14 203.6196044 0.5068066 -0.059875454 -0.016340357
## IC14_IC16 IC14_IC16 12.0374592 2.3981289 -0.131419580 0.065191885
## IC14_IC18 IC14_IC18 1.2864757 1.3938615 0.082481691 0.069255232
## IC14_IC20 IC14_IC20 9.5636043 3.2059464 0.109434809 -0.034469408
## IC15_IC17 IC15_IC17 0.4468222 0.3242601 0.039269703 -0.068463813
## IC17_IC18 IC17_IC18 13.6220191 1.7467207 0.127776852 -0.097275106
## IC18_IC19 IC18_IC19 1.5334740 12.0366791 -0.012813809 -0.028593224
## SCcorr_Disc.pval SCcorr_Rep.pval SCcorr.repBF RRBcorr_Disc.r
## IC01_IC12 0.67039832 0.87737433 0.6954284 0.022253885
## IC03_IC12 0.33308334 0.62869809 0.4613062 -0.052197602
## IC03_IC13 0.03345810 0.26358581 0.9918510 -0.100984042
## IC03_IC18 0.74840893 0.12681306 0.9684752 0.087762907
## IC04_IC06 0.08777141 0.09581035 0.1627004 -0.088447737
## IC04_IC11 0.33780635 0.85020459 0.5096450 0.001223994
## IC04_IC12 0.74868464 0.65673363 0.6674887 -0.018878034
## IC05_IC06 0.58586694 0.88893298 0.6776660 0.129883126
## IC05_IC19 0.30181386 0.73140964 0.4593306 0.111258537
## IC07_IC13 0.79557911 0.78982547 0.7256737 0.006504303
## IC08_IC11 0.11020834 0.02476101 8.0866009 0.140225485
## IC08_IC20 0.62977061 0.04189683 3.1215669 0.004305451
## IC11_IC12 0.34044681 0.21671088 1.4854833 -0.065262479
## IC12_IC17 0.26296847 0.02214415 7.0481993 0.268013929
## IC12_IC20 0.65142557 0.02996203 3.6324648 0.007883081
## IC13_IC14 0.68993704 0.93721109 0.6630706 -0.033549747
## IC14_IC16 0.18534161 0.32510632 0.2967348 -0.020705032
## IC14_IC18 0.27886889 0.32210933 1.1442546 0.052195690
## IC14_IC20 0.46732477 0.50302870 0.5359185 0.059448943
## IC15_IC17 0.93645920 0.28002795 0.9042265 0.059630717
## IC17_IC18 0.28318260 0.23107323 0.3945256 0.066930613
## IC18_IC19 0.57696224 0.60085236 0.8030510 0.078735341
## RRBcorr_Rep.r RRBcorr_Disc.pval RRBcorr_Rep.pval RRBcorr.repBF
## IC01_IC12 -0.031570729 0.67581534 0.662192433 0.7712664
## IC03_IC12 -0.268313828 0.53832312 0.009197724 8.0245238
## IC03_IC13 -0.105545423 0.32717839 0.330239493 1.1285575
## IC03_IC18 -0.191165725 0.28544500 0.120789313 0.4190989
## IC04_IC06 0.131186457 0.25946725 0.243491459 0.3703527
## IC04_IC11 -0.006080479 0.39683381 0.489930085 0.8848918
## IC04_IC12 -0.132022831 0.91914190 0.201782973 1.1339116
## IC05_IC06 0.008273737 0.30770101 0.990641170 0.5392303
## IC05_IC19 0.020045801 0.23452312 0.904463122 0.5252455
## IC07_IC13 0.080865498 0.85975739 0.704722665 0.7459577
## IC08_IC11 0.234578549 0.08988736 0.022725465 8.9081085
## IC08_IC20 0.135383657 0.80035822 0.252352808 0.8330652
## IC11_IC12 -0.029488716 0.54328656 0.571609043 0.8222671
## IC12_IC17 -0.111746204 0.03543136 0.471909306 0.1178531
## IC12_IC20 0.291944708 0.52286861 0.011605625 1.4242822
## IC13_IC14 0.240763309 0.94254345 0.005337477 4.5492368
## IC14_IC16 0.131510531 0.89989062 0.128004284 1.1475421
## IC14_IC18 0.206087538 0.34202584 0.015003756 8.0998250
## IC14_IC20 -0.196980403 0.89606118 0.038726525 1.8179576
## IC15_IC17 0.181375760 0.80383217 0.131115427 1.4923754
## IC17_IC18 -0.131431759 0.82941025 0.227572306 0.8816555
## IC18_IC19 -0.085669113 0.90785978 0.147221837 1.1002032
## SumSCRRB_Disc.r SumSCRRB_Rep.r SumSCRRB_Disc.pval SumSCRRB_Rep.pval
## IC01_IC12 0.02901956 -0.01216495 0.59639342 0.744267295
## IC03_IC12 0.05598002 -0.14992126 0.82324983 0.109489038
## IC03_IC13 -0.21386179 -0.14306998 0.05019802 0.205371003
## IC03_IC18 0.05422217 -0.20242756 0.37914842 0.070358059
## IC04_IC06 -0.15934662 0.18064959 0.06770220 0.080738597
## IC04_IC11 0.03906505 -0.01646374 0.27528630 0.921735250
## IC04_IC12 -0.04208466 -0.05450550 0.78758276 0.723083828
## IC05_IC06 0.05825782 0.01057013 0.82961723 0.926953069
## IC05_IC19 0.13048683 -0.01768556 0.18012260 0.853706029
## IC07_IC13 0.02407416 0.07926642 0.78434455 0.712548753
## IC08_IC11 0.16676443 0.24768499 0.04649155 0.007703423
## IC08_IC20 0.04615055 0.21130671 0.86148346 0.044697063
## IC11_IC12 -0.10848355 -0.10658917 0.32474187 0.261342137
## IC12_IC17 0.09204190 -0.20289733 0.63770172 0.055301730
## IC12_IC20 0.06138142 0.29703865 0.96451992 0.005006840
## IC13_IC14 -0.06286582 0.09966974 0.75457845 0.144709887
## IC14_IC16 -0.10620909 0.10922603 0.37770170 0.155192719
## IC14_IC18 0.09015854 0.14686474 0.18671367 0.054532693
## IC14_IC20 0.11370131 -0.11684976 0.59080811 0.141479102
## IC15_IC17 0.06380923 0.03307955 0.82805483 0.970847557
## IC17_IC18 0.13122882 -0.13299202 0.38186762 0.159515783
## IC18_IC19 0.03812920 -0.06083131 0.75817758 0.260814123
## SumSCRRB.repBF zds_Disc.r zds_Rep.r zds_Disc.pval zds_Rep.pval
## IC01_IC12 0.7303170 0.002657793 0.023309478 0.978621128 0.87424447
## IC03_IC12 1.1153558 0.136402752 0.136734419 0.155024347 0.14857512
## IC03_IC13 1.3712965 -0.107756293 -0.062880426 0.320029498 0.66763968
## IC03_IC18 0.5953742 -0.058854280 -0.035681414 0.570051184 0.66235397
## IC04_IC06 0.1326764 -0.061618574 0.082200387 0.551763355 0.36315105
## IC04_IC11 0.5455604 0.043233647 -0.014899531 0.876162830 0.55813481
## IC04_IC12 0.7448749 -0.022345781 0.092781160 0.845957187 0.15265506
## IC05_IC06 0.6863482 -0.112569212 0.004027948 0.193913203 0.88122595
## IC05_IC19 0.3926020 -0.003416208 -0.049681652 0.908621815 0.65702061
## IC07_IC13 0.7484291 0.018705713 0.006338976 0.934075525 0.99565767
## IC08_IC11 22.7219001 -0.001563469 0.042788914 0.967658120 0.52097367
## IC08_IC20 2.3252346 0.047133160 0.119371439 0.543994106 0.19797173
## IC11_IC12 1.3193817 -0.034806562 -0.109081175 0.735765943 0.38135921
## IC12_IC17 1.0775135 -0.266888039 -0.137767387 0.005822396 0.06242402
## IC12_IC20 4.8818904 0.059725308 0.037417649 0.428559577 0.51946125
## IC13_IC14 0.9384866 -0.026012431 -0.170165849 0.778505391 0.08031152
## IC14_IC16 0.5141249 -0.093700405 -0.016876321 0.277971435 0.91213814
## IC14_IC18 4.1581081 0.031713651 -0.060486527 0.824892929 0.53322281
## IC14_IC20 0.7598084 0.048866197 0.090168120 0.588162711 0.39621295
## IC15_IC17 0.6895181 -0.008920903 -0.185111864 0.909308101 0.03988013
## IC17_IC18 0.5153622 0.058791490 -0.015996310 0.462311380 0.72393577
## IC18_IC19 1.1256414 -0.064881831 0.025111679 0.578426142 0.67263808
## zds.repBF SumSCRRB_SCequalRRB_Disc.r SumSCRRB_SCequalRRB_Rep.r
## IC01_IC12 0.7031229 0.08453430 -0.051429808
## IC03_IC12 2.0059578 -0.01155316 -0.252101805
## IC03_IC13 0.7063224 -0.22792682 -0.070945668
## IC03_IC18 0.7661179 -0.14515754 -0.346254372
## IC04_IC06 0.6019329 -0.23983031 0.192596559
## IC04_IC11 0.7263378 0.07979150 -0.068780809
## IC04_IC12 1.0215623 0.00970710 -0.216566499
## IC05_IC06 0.5024849 0.08476974 -0.044465200
## IC05_IC19 0.7523901 0.26829860 -0.002427627
## IC07_IC13 0.6984898 -0.06478857 0.133901003
## IC08_IC11 0.7679910 0.20314909 0.237543592
## IC08_IC20 1.4464655 0.10572511 0.226051761
## IC11_IC12 0.9605286 -0.16341962 -0.217659882
## IC12_IC17 3.1878337 0.09933078 -0.232918057
## IC12_IC20 0.8578478 0.17634246 0.385294634
## IC13_IC14 1.9240908 -0.21411146 0.330555609
## IC14_IC16 0.5499091 -0.08390345 0.159239090
## IC14_IC18 0.7130210 0.08960937 0.244718725
## IC14_IC20 0.9848591 0.08161356 -0.220639840
## IC15_IC17 2.3174567 0.04856125 0.149623778
## IC17_IC18 0.5522379 0.09205547 -0.137009332
## IC18_IC19 0.6018958 0.09847882 -0.128455963
## SumSCRRB_SCequalRRB_Disc.pval SumSCRRB_SCequalRRB_Rep.pval
## IC01_IC12 0.56188888 0.974935179
## IC03_IC12 0.93872894 0.035689420
## IC03_IC13 0.10700501 0.570160190
## IC03_IC18 0.45189173 0.005517810
## IC04_IC06 0.03046698 0.165815584
## IC04_IC11 0.31151345 0.836345746
## IC04_IC12 0.94603287 0.110996817
## IC05_IC06 0.87292946 0.714012828
## IC05_IC19 0.08498255 0.963383500
## IC07_IC13 0.75442822 0.266238495
## IC08_IC11 0.04208890 0.071784762
## IC08_IC20 0.60674866 0.111946658
## IC11_IC12 0.24441293 0.108452055
## IC12_IC17 0.43803105 0.140246358
## IC12_IC20 0.43404386 0.003877243
## IC13_IC14 0.25593092 0.004580470
## IC14_IC16 0.28336103 0.227197717
## IC14_IC18 0.26456122 0.012195743
## IC14_IC20 0.82365218 0.090297943
## IC15_IC17 0.83349736 0.323829438
## IC17_IC18 0.99329924 0.405920958
## IC18_IC19 0.93109548 0.292075276
## SumSCRRB_SCequalRRB.repBF zds_SCequalRRB_Disc.r zds_SCequalRRB_Rep.r
## IC01_IC12 0.63757610 -0.304764964 0.007186694
## IC03_IC12 2.38392316 0.053429273 0.238823062
## IC03_IC13 0.61429155 0.056651156 -0.048901495
## IC03_IC18 13.08618251 -0.101232305 -0.034137618
## IC04_IC06 0.07384865 -0.108473930 0.009091615
## IC04_IC11 0.48880187 -0.082045454 0.029230550
## IC04_IC12 1.42618135 0.043130364 0.019854650
## IC05_IC06 0.69969425 -0.062024451 0.046155169
## IC05_IC19 0.30896018 0.059003703 0.024552826
## IC07_IC13 0.78795137 -0.136142503 0.059794448
## IC08_IC11 3.58418088 -0.034850679 0.045497331
## IC08_IC20 1.90443486 0.181212224 -0.043494587
## IC11_IC12 2.48406719 0.084597673 0.061218318
## IC12_IC17 0.59018732 -0.150194639 -0.235150588
## IC12_IC20 16.66623696 -0.042696936 -0.139094881
## IC13_IC14 0.79291236 -0.315130289 0.004083003
## IC14_IC16 0.39291048 -0.158107047 0.002437298
## IC14_IC18 10.85032508 -0.255930003 -0.032115490
## IC14_IC20 1.20057044 0.080616740 0.106186082
## IC15_IC17 0.98902056 -0.019421667 -0.321794127
## IC17_IC18 0.83935203 0.065140888 -0.150301757
## IC18_IC19 0.88977031 -0.000418521 0.136700449
## zds_SCequalRRB_Disc.pval zds_SCequalRRB_Rep.pval zds_SCequalRRB.repBF
## IC01_IC12 0.010689632 0.878332731 0.15178244
## IC03_IC12 0.669109307 0.044852452 2.92923690
## IC03_IC13 0.535414365 0.777230467 0.59291183
## IC03_IC18 0.456688706 0.769581356 0.69309880
## IC04_IC06 0.523002532 0.928086526 0.61291571
## IC04_IC11 0.271810462 0.974263725 0.50296076
## IC04_IC12 0.631124715 0.735180922 0.73826492
## IC05_IC06 0.755680619 0.767550870 0.66639479
## IC05_IC19 0.723850643 0.788327041 0.72427852
## IC07_IC13 0.208234888 0.725260931 0.38410749
## IC08_IC11 0.434726334 0.554933175 0.51876484
## IC08_IC20 0.041248059 0.733317305 0.16871633
## IC11_IC12 0.536583057 0.456206960 0.92429365
## IC12_IC17 0.302068917 0.029547179 5.72240258
## IC12_IC20 0.974005398 0.354297525 0.88759179
## IC13_IC14 0.004342648 0.978347811 0.07565999
## IC14_IC16 0.305900575 0.990160505 0.52988358
## IC14_IC18 0.022048419 0.717548856 0.27604698
## IC14_IC20 0.470824746 0.425928307 0.96437838
## IC15_IC17 0.810102206 0.003769356 8.76381163
## IC17_IC18 0.259002731 0.134704708 0.38382072
## IC18_IC19 0.732690251 0.208950037 1.27274777
## zds_SCoverRRB_Disc.r zds_SCoverRRB_Rep.r zds_SCoverRRB_Disc.pval
## IC01_IC12 0.12514764 -0.01383797 0.457925466
## IC03_IC12 0.31483942 -0.09835797 0.038041964
## IC03_IC13 -0.35132901 -0.05521216 0.037758465
## IC03_IC18 0.15002844 0.07306033 0.418197561
## IC04_IC06 0.16360124 -0.02879048 0.436984271
## IC04_IC11 0.18858525 0.03255199 0.137162322
## IC04_IC12 -0.09421679 -0.02818725 0.883365684
## IC05_IC06 -0.11190641 0.05835806 0.539267142
## IC05_IC19 -0.22911017 -0.04303200 0.241649389
## IC07_IC13 0.49912811 -0.04158595 0.001538571
## IC08_IC11 -0.27380487 -0.22139803 0.100054802
## IC08_IC20 0.07577193 0.10609126 0.856389971
## IC11_IC12 -0.15366924 -0.06426293 0.505381795
## IC12_IC17 -0.48501988 -0.07174130 0.002428651
## IC12_IC20 0.27712226 0.17403741 0.165586954
## IC13_IC14 0.01863420 -0.26895060 0.895098975
## IC14_IC16 -0.32092795 -0.08336991 0.064032091
## IC14_IC18 -0.07593056 -0.16472460 0.732498079
## IC14_IC20 0.22447372 -0.06365687 0.151820584
## IC15_IC17 -0.22142959 -0.44367638 0.075975895
## IC17_IC18 0.17504276 -0.08765311 0.310036782
## IC18_IC19 0.16724776 0.04113726 0.350918942
## zds_SCoverRRB_Rep.pval zds_SCoverRRB.repBF VinelandABC_Disc.r
## IC01_IC12 0.883286514 0.57697056 0.117928538
## IC03_IC12 0.512752442 0.12125287 -0.072641630
## IC03_IC13 0.667595070 0.36356118 -0.134772608
## IC03_IC18 0.682208585 0.73059618 -0.061046059
## IC04_IC06 0.887113396 0.56890841 0.071190982
## IC04_IC11 0.854583787 0.45261707 -0.103254277
## IC04_IC12 0.870999053 0.70866468 0.002441248
## IC05_IC06 0.664033284 0.58228054 0.057668657
## IC05_IC19 0.769214412 0.59585482 -0.143747879
## IC07_IC13 0.888895622 0.03157664 0.073694498
## IC08_IC11 0.129081609 2.28966778 -0.041406876
## IC08_IC20 0.499225579 0.83225718 -0.012128605
## IC11_IC12 0.710768595 0.73294943 0.056106901
## IC12_IC17 0.599942240 0.12966061 0.016795507
## IC12_IC20 0.252394877 1.34700523 0.027880754
## IC13_IC14 0.086989107 1.69900539 0.025951362
## IC14_IC16 0.586797138 0.50748612 0.202370386
## IC14_IC18 0.307540628 1.06590557 0.118454122
## IC14_IC20 0.761341021 0.33330483 -0.182685897
## IC15_IC17 0.003992099 44.91523696 -0.025714298
## IC17_IC18 0.608602301 0.43959511 -0.162183284
## IC18_IC19 0.770715661 0.65484908 -0.095770335
## VinelandABC_Rep.r VinelandABC_Disc.pval VinelandABC_Rep.pval
## IC01_IC12 0.177064715 0.20008779 0.067791395
## IC03_IC12 0.007354335 0.36854945 0.976017651
## IC03_IC13 0.075485086 0.22800372 0.320234088
## IC03_IC18 -0.002086721 0.68570212 0.950347592
## IC04_IC06 -0.250785296 0.45632415 0.005964628
## IC04_IC11 -0.112075279 0.23611656 0.211706170
## IC04_IC12 -0.131798765 0.92509074 0.222921592
## IC05_IC06 0.039613563 0.73340254 0.756818969
## IC05_IC19 -0.211936396 0.13287524 0.026487633
## IC07_IC13 -0.046360656 0.52157295 0.522445833
## IC08_IC11 0.007092690 0.77280316 0.764259781
## IC08_IC20 -0.214624658 0.95294781 0.017210910
## IC11_IC12 0.056210538 0.55274527 0.479505554
## IC12_IC17 0.053290790 0.82865168 0.705244219
## IC12_IC20 -0.077436310 0.82065386 0.423044058
## IC13_IC14 0.026756794 0.68294229 0.731917061
## IC14_IC16 -0.075385606 0.04684481 0.505927395
## IC14_IC18 0.062231634 0.22321958 0.441414333
## IC14_IC20 -0.218013422 0.04074162 0.011970586
## IC15_IC17 -0.097459593 0.65371858 0.231414244
## IC17_IC18 -0.065270821 0.08848605 0.383315323
## IC18_IC19 0.141866569 0.26986009 0.131402514
## VinelandABC.repBF VinelandABC_SCequalRRB_Disc.r
## IC01_IC12 3.5207973 0.1067004255
## IC03_IC12 0.5607317 -0.1006967711
## IC03_IC13 0.3406041 -0.2073287286
## IC03_IC18 0.6802684 0.0144610042
## IC04_IC06 1.4756638 -0.0568329390
## IC04_IC11 1.5367022 -0.1012964197
## IC04_IC12 0.9670556 -0.1555691202
## IC05_IC06 0.7353533 0.1376289705
## IC05_IC19 7.4441019 -0.2372982915
## IC07_IC13 0.5695473 0.1002723097
## IC08_IC11 0.6711232 -0.3478406641
## IC08_IC20 3.2002400 -0.1796873079
## IC11_IC12 0.8989774 -0.1793628342
## IC12_IC17 0.7492798 -0.1076469471
## IC12_IC20 0.7447499 -0.0006189235
## IC13_IC14 0.7423939 0.0191741885
## IC14_IC16 0.1446152 0.2976287607
## IC14_IC18 0.8937073 0.1247968842
## IC14_IC20 16.1666432 -0.2929031944
## IC15_IC17 1.2606998 0.0579360643
## IC17_IC18 0.8498884 -0.0794348038
## IC18_IC19 0.3986396 -0.1249865158
## VinelandABC_SCequalRRB_Rep.r VinelandABC_SCequalRRB_Disc.pval
## IC01_IC12 -0.028225551 0.474313869
## IC03_IC12 -0.062394517 0.460526571
## IC03_IC13 0.028136233 0.174620368
## IC03_IC18 0.081908225 0.639641254
## IC04_IC06 -0.378369363 0.618756230
## IC04_IC11 0.203348437 0.392475830
## IC04_IC12 -0.129102367 0.286922336
## IC05_IC06 0.196632228 0.428014151
## IC05_IC19 -0.343753200 0.056085994
## IC07_IC13 -0.129714056 0.573378368
## IC08_IC11 0.109095820 0.006745789
## IC08_IC20 -0.380912406 0.232980570
## IC11_IC12 0.053705671 0.177836017
## IC12_IC17 0.100371752 0.612022813
## IC12_IC20 -0.003099587 0.843525917
## IC13_IC14 0.043940453 0.785993679
## IC14_IC16 -0.045257523 0.028797530
## IC14_IC18 0.008059356 0.341305658
## IC14_IC20 -0.294362907 0.022108601
## IC15_IC17 -0.106195933 0.868268122
## IC17_IC18 -0.093917095 0.557881568
## IC18_IC19 0.157557469 0.253719517
## VinelandABC_SCequalRRB_Rep.pval VinelandABC_SCequalRRB.repBF
## IC01_IC12 0.751955781 0.56125646
## IC03_IC12 0.503428338 0.87585412
## IC03_IC13 0.672151604 0.33929349
## IC03_IC18 0.556072065 0.83252710
## IC04_IC06 0.001184757 23.41869444
## IC04_IC11 0.125166136 0.54954983
## IC04_IC12 0.429535418 0.93927505
## IC05_IC06 0.142103918 1.87510553
## IC05_IC19 0.004279808 38.01376662
## IC07_IC13 0.234370431 0.66420106
## IC08_IC11 0.241339263 0.02836349
## IC08_IC20 0.001022701 61.02756904
## IC11_IC12 0.437243122 0.30097777
## IC12_IC17 0.459897734 0.62415799
## IC12_IC20 0.988957004 0.69163219
## IC13_IC14 0.553336609 0.81526491
## IC14_IC16 0.733278256 0.14004165
## IC14_IC18 0.833248475 0.61992832
## IC14_IC20 0.010024113 20.86007442
## IC15_IC17 0.223450591 0.92236744
## IC17_IC18 0.351932765 1.05624652
## IC18_IC19 0.191276991 0.36574951
## VinelandABC_SCoverRRB_Disc.r VinelandABC_SCoverRRB_Rep.r
## IC01_IC12 0.17450773 0.536418218
## IC03_IC12 -0.05829850 0.165714100
## IC03_IC13 -0.05493026 0.076907406
## IC03_IC18 -0.13645941 -0.176766966
## IC04_IC06 0.23460265 -0.026735180
## IC04_IC11 -0.10925131 -0.488919436
## IC04_IC12 0.15073623 -0.163987447
## IC05_IC06 -0.01252665 -0.131175026
## IC05_IC19 -0.05625207 -0.023597413
## IC07_IC13 0.06692883 0.055654422
## IC08_IC11 0.24185171 -0.158762892
## IC08_IC20 0.12353340 0.067336298
## IC11_IC12 0.30720503 0.005041906
## IC12_IC17 0.14696804 -0.096416357
## IC12_IC20 0.06309952 -0.193767438
## IC13_IC14 0.01091215 -0.012687804
## IC14_IC16 0.11380876 -0.107986615
## IC14_IC18 0.07760485 0.260483377
## IC14_IC20 -0.06185320 -0.046077450
## IC15_IC17 -0.11811210 -0.077664969
## IC17_IC18 -0.27698340 -0.042239434
## IC18_IC19 -0.04723721 0.126906127
## VinelandABC_SCoverRRB_Disc.pval VinelandABC_SCoverRRB_Rep.pval
## IC01_IC12 0.25646580 0.0003763654
## IC03_IC12 0.58902280 0.2862861949
## IC03_IC13 0.77979061 0.5361854701
## IC03_IC18 0.44624818 0.3135064402
## IC04_IC06 0.13348894 0.8073516501
## IC04_IC11 0.42462069 0.0009068565
## IC04_IC12 0.37583455 0.3097789492
## IC05_IC06 0.91621952 0.3970113293
## IC05_IC19 0.75496364 0.9903664593
## IC07_IC13 0.77300968 0.7772829569
## IC08_IC11 0.09388985 0.3548774338
## IC08_IC20 0.46001880 0.7488366371
## IC11_IC12 0.04726877 0.9085844864
## IC12_IC17 0.36645122 0.6246726416
## IC12_IC20 0.65746863 0.1681895928
## IC13_IC14 0.83088998 0.9295495843
## IC14_IC16 0.42424047 0.5332027590
## IC14_IC18 0.58348099 0.0935084324
## IC14_IC20 0.59893938 0.7317663965
## IC15_IC17 0.52374645 0.6277929619
## IC17_IC18 0.06414327 0.6793765600
## IC18_IC19 0.84327532 0.4175585985
## VinelandABC_SCoverRRB.repBF
## IC01_IC12 152.6207509
## IC03_IC12 0.6511909
## IC03_IC13 0.6945975
## IC03_IC18 1.1612551
## IC04_IC06 0.3246640
## IC04_IC11 51.7317298
## IC04_IC12 0.4726053
## IC05_IC06 0.8804839
## IC05_IC19 0.6811388
## IC07_IC13 0.7290715
## IC08_IC11 0.1869120
## IC08_IC20 0.7040206
## IC11_IC12 0.2743429
## IC12_IC17 0.4808452
## IC12_IC20 0.8014064
## IC13_IC14 0.6868982
## IC14_IC16 0.5094492
## IC14_IC18 2.1857454
## IC14_IC20 0.7359131
## IC15_IC17 0.7841706
## IC17_IC18 0.4343160
## IC18_IC19 0.7588176
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
SCcorr_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCcorr_Disc_mat) = comps
colnames(SCcorr_Disc_mat) = comps
diag(SCcorr_Disc_mat) = 0
SCcorr_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCcorr_Rep_mat) = comps
colnames(SCcorr_Rep_mat) = comps
diag(SCcorr_Rep_mat) = 0
RRBcorr_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(RRBcorr_Disc_mat) = comps
colnames(RRBcorr_Disc_mat) = comps
diag(RRBcorr_Disc_mat) = 0
RRBcorr_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(RRBcorr_Rep_mat) = comps
colnames(RRBcorr_Rep_mat) = comps
diag(RRBcorr_Rep_mat) = 0
SumSCRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SumSCRRB_Disc_mat) = comps
colnames(SumSCRRB_Disc_mat) = comps
diag(SumSCRRB_Disc_mat) = 0
SumSCRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SumSCRRB_Rep_mat) = comps
colnames(SumSCRRB_Rep_mat) = comps
diag(SumSCRRB_Rep_mat) = 0
zds_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_Disc_mat) = comps
colnames(zds_Disc_mat) = comps
diag(zds_Disc_mat) = 0
zds_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_Rep_mat) = comps
colnames(zds_Rep_mat) = comps
diag(zds_Rep_mat) = 0
zds_SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCequalRRB_Disc_mat) = comps
colnames(zds_SCequalRRB_Disc_mat) = comps
diag(zds_SCequalRRB_Disc_mat) = 0
zds_SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCequalRRB_Rep_mat) = comps
colnames(zds_SCequalRRB_Rep_mat) = comps
diag(zds_SCequalRRB_Rep_mat) = 0
zds_SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCoverRRB_Disc_mat) = comps
colnames(zds_SCoverRRB_Disc_mat) = comps
diag(zds_SCoverRRB_Disc_mat) = 0
zds_SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCoverRRB_Rep_mat) = comps
colnames(zds_SCoverRRB_Rep_mat) = comps
diag(zds_SCoverRRB_Rep_mat) = 0
VinelandABC_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_Disc_mat) = comps
colnames(VinelandABC_Disc_mat) = comps
diag(VinelandABC_Disc_mat) = 0
VinelandABC_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_Rep_mat) = comps
colnames(VinelandABC_Rep_mat) = comps
diag(VinelandABC_Rep_mat) = 0
VinelandABC_SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCequalRRB_Disc_mat) = comps
colnames(VinelandABC_SCequalRRB_Disc_mat) = comps
diag(VinelandABC_SCequalRRB_Disc_mat) = 0
VinelandABC_SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCequalRRB_Rep_mat) = comps
colnames(VinelandABC_SCequalRRB_Rep_mat) = comps
diag(VinelandABC_SCequalRRB_Rep_mat) = 0
VinelandABC_SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCoverRRB_Disc_mat) = comps
colnames(VinelandABC_SCoverRRB_Disc_mat) = comps
diag(VinelandABC_SCoverRRB_Disc_mat) = 0
VinelandABC_SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCoverRRB_Rep_mat) = comps
colnames(VinelandABC_SCoverRRB_Rep_mat) = comps
diag(VinelandABC_SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCcorr.repBF"]>10 &
aovres[comp_pair,"SCcorr_Disc.pval"]<0.05 &
aovres[comp_pair,"SCcorr_Rep.pval"]<0.05){
SCcorr_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCcorr_Disc.r"]
SCcorr_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCcorr_Rep.r"]
} else{
SCcorr_Disc_mat[comp1,comp2] = 0.0001
SCcorr_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"RRBcorr.repBF"]>10 &
aovres[comp_pair,"RRBcorr_Disc.pval"]<0.05 &
aovres[comp_pair,"RRBcorr_Rep.pval"]<0.05){
RRBcorr_Disc_mat[comp1,comp2] = aovres[comp_pair,"RRBcorr_Disc.r"]
RRBcorr_Rep_mat[comp1,comp2] = aovres[comp_pair,"RRBcorr_Rep.r"]
} else{
RRBcorr_Disc_mat[comp1,comp2] = 0.0001
RRBcorr_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SumSCRRB.repBF"]>10 &
aovres[comp_pair,"SumSCRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"SumSCRRB_Rep.pval"]<0.05){
SumSCRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SumSCRRB_Disc.r"]
SumSCRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SumSCRRB_Rep.r"]
} else{
SumSCRRB_Disc_mat[comp1,comp2] = 0.0001
SumSCRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"zds.repBF"]>10 &
aovres[comp_pair,"zds_Disc.pval"]<0.05 &
aovres[comp_pair,"zds_Rep.pval"]<0.05){
zds_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_Disc.r"]
zds_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_Rep.r"]
} else{
zds_Disc_mat[comp1,comp2] = 0.0001
zds_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"zds_SCequalRRB.repBF"]>10 &
aovres[comp_pair,"zds_SCequalRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"zds_SCequalRRB_Rep.pval"]<0.05){
zds_SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_SCequalRRB_Disc.r"]
zds_SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_SCequalRRB_Rep.r"]
} else{
zds_SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
zds_SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"zds_SCoverRRB.repBF"]>10 &
aovres[comp_pair,"zds_SCoverRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"zds_SCoverRRB_Rep.pval"]<0.05){
zds_SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_SCoverRRB_Disc.r"]
zds_SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_SCoverRRB_Rep.r"]
} else{
zds_SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
zds_SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"VinelandABC.repBF"]>10 &
aovres[comp_pair,"VinelandABC_Disc.pval"]<0.05 &
aovres[comp_pair,"VinelandABC_Rep.pval"]<0.05){
VinelandABC_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_Disc.r"]
VinelandABC_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_Rep.r"]
} else{
VinelandABC_Disc_mat[comp1,comp2] = 0.0001
VinelandABC_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"VinelandABC_SCequalRRB.repBF"]>10 &
aovres[comp_pair,"VinelandABC_SCequalRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"VinelandABC_SCequalRRB_Rep.pval"]<0.05){
VinelandABC_SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCequalRRB_Disc.r"]
VinelandABC_SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCequalRRB_Rep.r"]
} else{
VinelandABC_SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
VinelandABC_SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"VinelandABC_SCoverRRB.repBF"]>10 &
aovres[comp_pair,"VinelandABC_SCoverRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"VinelandABC_SCoverRRB_Rep.pval"]<0.05){
VinelandABC_SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCoverRRB_Disc.r"]
VinelandABC_SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCoverRRB_Rep.r"]
} else{
VinelandABC_SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
VinelandABC_SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCcorr_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCcorr_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(RRBcorr_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(RRBcorr_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SumSCRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SumSCRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
#------------------------------------------------------------------------------
# Consensus Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_consensusPairs = c("IC07_IC13","IC03_IC12")
SCoverRRB_consensusPairs = c("IC12_IC17")
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (is.element(comp_pair,SCequalRRB_consensusPairs)){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
} # if
if (is.element(comp_pair,SCoverRRB_consensusPairs)){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
} # if
} # for
grid.col = c(IC01 = "grey",
IC03 = "green",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "green",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "green",
IC13 = "green",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "blue",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "blue",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar